Upload Qwen 2.5 3B Instruct model checkpoint

.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+test_data/gsm8k_test filter=lfs diff=lfs merge=lfs -text
+verl/data/deepscaler_uniform_train_3004.json filter=lfs diff=lfs merge=lfs -text

.github/dependabot.yml

@@ -0,0 +1,9 @@
+## Enabled the dependabot to check the dependencies of the project
+## Dependabot will open pull requests to update dependencies automatically
+
+version: 2
+updates:
+  - package-ecosystem: pip
+    directory: "/"
+    schedule:
+      interval: weekly

.github/workflows/checkpoints.yml

@@ -0,0 +1,64 @@
+name: checkpoints
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/checkpoints.yml
+      - "tests/checkpoint/*.sh"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_gsm8k_megatron:
+    runs-on: [self-hosted, l20-0]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: whatcanyousee/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te2.0-megatron0.11.0-v0.0.6
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test]
+      - name: Prepare gsm8k dataset
+        run: |
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running Checkpoint Integration Test (Qwen Megatron)
+        run: |
+          ray stop --force
+          export PYTHONPATH=$PYTHONPATH:/opt/nvidia/Megatron-LM
+          bash tests/checkpoint/run_qwen_megatron_ckpt.sh
+      - name: Running Checkpoint Integration Test (Deepseek Megatron)
+        run: |
+          ray stop --force
+          export PYTHONPATH=$PYTHONPATH:/opt/nvidia/Megatron-LM
+          bash tests/checkpoint/run_deepseek_megatron_ckpt.sh
+      - name: Test Megatron checkpoints merging function (Qwen Actor and Critic)
+        run: |
+          python scripts/model_merger.py --backend megatron --tie-word-embedding --hf_model_path Qwen/Qwen2.5-0.5B --local_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/actor --test --test_hf_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/actor/huggingface
+          python scripts/model_merger.py --backend megatron --is-value-model --hf_model_path Qwen/Qwen2.5-0.5B --local_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/critic --test --test_hf_dir checkpoints/verl_megatron_gsm8k_examples/qwen2_5_0b5_megatron_saveload/global_step_1/critic/huggingface
+      - name: Test Megatron checkpoints merging function (Deepseek Actor and Critic)
+        run: |
+          python scripts/model_merger.py --backend megatron --hf_model_path deepseek-ai/deepseek-coder-1.3b-instruct --local_dir checkpoints/verl_megatron_gsm8k_examples/deepseek_megatron_checkpoint_saveload/global_step_1/actor --test --test_hf_dir checkpoints/verl_megatron_gsm8k_examples/deepseek_megatron_checkpoint_saveload/global_step_1/actor/huggingface
+          python scripts/model_merger.py --backend megatron --is-value-model --hf_model_path deepseek-ai/deepseek-coder-1.3b-instruct --local_dir checkpoints/verl_megatron_gsm8k_examples/deepseek_megatron_checkpoint_saveload/global_step_1/critic --test --test_hf_dir checkpoints/verl_megatron_gsm8k_examples/deepseek_megatron_checkpoint_saveload/global_step_1/critic/huggingface

.github/workflows/dataset.yml

@@ -0,0 +1,56 @@
+name: dataset
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "verl/utils/dataset/*.py"
+      - .github/workflows/dataset.yml
+      - "!verl/workers/fsdp_workers.py"
+      - "!verl/workers/megatron_workers.py"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  ray:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 10 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip install hf_transfer
+          pip install -e .[test]
+          pip install --upgrade "ray>=2.40.0"
+          pip install cupy-cuda12x
+      - name: Running dataset tests
+        run: |
+          [ ! -d "$HOME/verl-data" ] && git clone --depth 1 https://github.com/eric-haibin-lin/verl-data ~/verl-data
+          pytest -s -x tests/verl/utils/dataset/test_rl_dataset.py
+          pytest -s -x tests/verl/utils/dataset/test_sft_dataset.py
+#          pytest -s -x tests/verl/utils/dataset/test_rm_dataset.py
+      - name: Running ray test using cupy (move it to L20 when dockerfile ready)
+        run: |
+          cd tests/ray
+          pytest -s -x test_rvdz.py

.github/workflows/e2e_ascend.yml

@@ -0,0 +1,47 @@
+name: e2e_ascend
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - .github/workflows/e2e_ascend.yml
+
+permissions:
+  contents: read
+  
+jobs:
+  test:
+    name: verl Ascend test (self-host)
+    runs-on: [self-hosted, npu-0]
+    timeout-minutes: 5 # Increase this timeout value as needed
+    env:
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: quay.io/ascend/cann:8.0.0-910b-ubuntu22.04-py3.10
+      volumes:
+        - /usr/local/dcmi:/usr/local/dcmi
+        - /usr/local/bin/npu-smi:/usr/local/bin/npu-smi
+        - /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/
+        # Use self-host cache speed up pip and model download
+        # - /home/action/actions-runner/_work/cache:/github/home/.cache/
+      options: >-
+        --device /dev/davinci0
+        --device /dev/davinci_manager
+        --device /dev/devmm_svm
+        --device /dev/hisi_hdc
+        --privileged
+        --network "host"
+    steps:
+      - name: Check npu and CANN info
+        run: |
+          cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info
+          npu-smi info
+      - name: Checkout volcengine/verl repo
+        uses: actions/checkout@v4
+      - name: Run test
+        run: |
+          lscpu

.github/workflows/e2e_eval_aime24.yml

@@ -0,0 +1,48 @@
+name: e2e_eval_aime24
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_eval_aime24.yml
+      - "tests/e2e/run_r1_distill_qwen_aime24_eval.sh"
+      - "!verl/trainer/main_ppo.py"
+      - "!verl/trainer/fsdp_sft_trainer.py"
+
+# Declare permissions just read content.
+permissions:
+  contents: read
+
+jobs:
+  e2e_eval_aime24:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: hiyouga/verl:ngc-th2.6.0-cu120-vllm0.8.2
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,gpu,math]
+      - name: Prepare aime24 dataset
+        run: |
+          ray stop --force
+          python3 recipe/r1/data_process.py --task aime2024
+      - name: Running generation and evaluation in aime2024
+        run: | 
+          ray stop --force
+          bash tests/e2e/run_r1_distill_qwen_aime24_eval.sh

.github/workflows/e2e_grpo.yml

@@ -0,0 +1,86 @@
+name: e2e_grpo
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_grpo.yml
+      - "tests/e2e/*.sh"
+      - "!verl/trainer/fsdp_sft_trainer.py"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_gsm8k_megatron-l20-0:
+    runs-on: [self-hosted, l20-0]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: whatcanyousee/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te2.0-megatron0.11.0-v0.0.6
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test]
+      - name: Prepare gsm8k dataset
+        run: |
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running GRPO gsm8k e2e training tests with FSDP on 8 L20 GPUs (Qwen)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_grpo.sh
+      - name: Running GRPO gsm8k e2e training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_grpo_megatron.sh
+  e2e_gsm8k_megatron-l20-1:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: whatcanyousee/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te2.0-megatron0.11.0-v0.0.6
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test]
+      - name: Prepare gsm8k dataset
+        run: |
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running GRPO gsm8k e2e training tests with FSDP on 8 L20 GPUs (Deepseek)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_deepseek_grpo.sh
+      - name: Running GRPO gsm8k e2e training tests with 3D parallelism on 8 L20 GPUs with Megatron (Deepseek)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_deepseek_grpo_megatron.sh

.github/workflows/e2e_gsm8k.yml

@@ -0,0 +1,95 @@
+name: e2e_gsm8k
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_gsm8k.yml
+      - "tests/e2e/*gsm8k*.sh"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_gsm8k:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: hiyouga/verl:ngc-th2.6.0-cu120-vllm0.8.2
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,gpu]
+      - name: Prepare gsm8k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using function rm and save ckpt
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_function_rm.sh
+      - name: Running gsm8k e2e without rmpad using function rm and load ckpt from previous step
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_function_rm_no_rmpad.sh
+          rm -rf ~/ckpt/*
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using function rm (GRPO)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_function_rm_grpo.sh
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using function rm (ReMax)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_function_rm_remax.sh
+      - name: Running gsm8k e2e with rmpad using model rm
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_model_rm.sh
+      - name: Running gsm8k e2e without rmpad using model rm
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_model_rm_no_rmpad.sh
+      - name: Running gsm8k e2e with rmpad using model rm and ulysses sp=2
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_model_rm_ulysses.sh
+      - name: Running gsm8k e2e with rmpad using model rm and dynamic batch size
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_model_rm_seq_balance.sh
+      - name: Running gsm8k e2e with rmpad using model rm with Liger Kernel enabled
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_model_rm_liger_kernel.sh
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using customized reward function
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_custom_function_rm.sh
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using function rm with in-reward kl and kl loss
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_function_rm_both_kl.sh
+

.github/workflows/e2e_gsm8k_dapo.yml

@@ -0,0 +1,50 @@
+name: e2e_gsm8k_dapo
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_gsm8k_dapo.yml
+      - "tests/e2e/*dapo.sh"
+      - "!verl/trainer/main_ppo.py"
+      - "!verl/trainer/fsdp_sft_trainer.py"
+      - "!verl/workers/megatron_workers.py"
+
+# Declare permissions just read content.
+permissions:
+  contents: read
+
+jobs:
+  e2e_gsm8k_dapo:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,gpu]
+      - name: Prepare gsm8k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e with dapo alg
+        run: | 
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_dapo.sh

.github/workflows/e2e_gsm8k_megatron.yml

@@ -0,0 +1,58 @@
+name: e2e_gsm8k_megatron
+# latest version: Megatron-LM core_r0.11.0 https://github.com/NVIDIA/Megatron-LM/tree/core_r0.11.0
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.3.x
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_gsm8k_megatron.yml
+      - "tests/e2e/*megatron*.sh"
+      - "!verl/workers/fsdp_workers.py"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_gsm8k_megatron:
+    runs-on: [self-hosted, l20-0]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: whatcanyousee/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te2.0-megatron0.11.0-v0.0.6
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test]
+      - name: Prepare gsm8k dataset
+        run: |
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e training tests with 3D parallelism on 8 L20 GPUs with Megatron (Deepseek)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_deepseek_megatron_parallelism.sh
+      - name: Running gsm8k e2e training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen)
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_megatron_parallelism.sh

.github/workflows/e2e_gsm8k_prime.yml

@@ -0,0 +1,50 @@
+name: e2e_gsm8k_prime
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_gsm8k_prime.yml
+      - "tests/e2e/*prime.sh"
+      - "!verl/trainer/main_ppo.py"
+      - "!verl/trainer/fsdp_sft_trainer.py"
+      - "!verl/workers/megatron_workers.py"
+
+# Declare permissions just read content.
+permissions:
+  contents: read
+
+jobs:
+  e2e_gsm8k_prime:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: hiyouga/verl:ngc-th2.6.0-cu120-vllm0.8.2
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,gpu]
+      - name: Prepare gsm8k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e with prime alg
+        run: | 
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_prime.sh

.github/workflows/e2e_lora.yml

@@ -0,0 +1,54 @@
+name: e2e_lora
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/e2e_lora.yml
+      - "tests/sft/*"
+      - "!verl/trainer/main_ppo.py"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_lora:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 5 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer peft
+          pip3 install -e .[test]
+      - name: Prepare gsm8k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e training tests with LoRA
+        run: |
+          ray stop --force
+          bash tests/sft/run_sft_qwen05_peft.sh 8 $HOME/ckpts/
+          rm -rf $HOME/ckpts/*

.github/workflows/e2e_sft.yml

@@ -0,0 +1,66 @@
+name: e2e_sft
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/e2e_sft.yml
+      - "tests/e2e/*.sh"
+      - "!verl/trainer/main_ppo.py"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_sft:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 5 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,gpu]
+      - name: Prepare gsm8k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using function rm
+        run: |
+          ray stop --force
+          bash tests/sft/run_sft.sh
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with sequence parallism
+        run: |
+          ray stop --force
+          bash examples/sft/gsm8k/run_qwen_05_sp2.sh 8 $HOME/ckpts/
+      - name: Check loss difference between sequence parallel vs. default implementation
+        run: |
+          ray stop --force
+          bash tests/sft/run_sft_sp_loss_match.sh
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with sequence parallism and liger
+        run: |
+          ray stop --force
+          bash tests/sft/run_sft_qwen05_sp2_liger.sh 8 $HOME/ckpts/
+          rm -rf $HOME/ckpts/

.github/workflows/e2e_sglang_gsm8k.yml

@@ -0,0 +1,53 @@
+name: e2e_sglang_gsm8k
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/e2e_sglang_gsm8k.yml
+      - "tests/e2e/*.sh"
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_sglang_gsm8k:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 40 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: ocss884/verl-sglang:ngc-th2.5.1-cu126-sglang0.4.4.post4
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,gpu,sglang] --no-deps
+      - name: Prepare gsm8k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/gsm8k.py
+      - name: Running gsm8k e2e training tests on 8 L20 GPUs with rmpad using function rm and save ckpt
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen_gsm8k_function_rm.sh sglang
+

.github/workflows/e2e_vlm_geo3k.yml

@@ -0,0 +1,48 @@
+name: e2e_vlm_geo3k
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.3.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/e2e_vlm_geo3k.yml
+      - "tests/e2e/*vl*.sh"
+      - "!recipe/**"
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  e2e_vlm_geo3k:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 10 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: hiyouga/verl:ngc-th2.6.0-cu120-vllm0.8.2
+      options: --gpus all --shm-size=40g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,geo,vllm]
+          python -c "import transformers; print(transformers.__version__)"
+      - name: Prepare geo3k dataset
+        run: |
+          ray stop --force
+          python3 examples/data_preprocess/geo3k.py
+      - name: Running geo3k vlm e2e training tests on 8 L20 GPUs with rmpad using function rm
+        run: |
+          ray stop --force
+          bash tests/e2e/run_qwen2vl_geo3k_function_rm.sh

.github/workflows/model.yml

@@ -0,0 +1,76 @@
+name: model_rmpad
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.3.x
+    paths:
+      - "**/*.py"
+      - "tests/model/*"
+      - .github/workflows/model.yml
+      - "!recipe/**"
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  model_rmpad:
+    runs-on: [self-hosted, l20-1]
+    timeout-minutes: 20 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository and upgrade to latest transformers/flash_attn
+        run: |
+          pip3 install -e .[test]
+          pip3 install --upgrade transformers
+      - name: Running rmpad model tests on 8 L20 GPUs + flash_attn 2.5.8
+        run: |
+          pytest -s tests/model/test_transformer.py
+      - name: Running rmpad model tests on 8 L20 GPUs + latest flash_attn
+        run: |
+          pip3 install --upgrade flash_attn --no-build-isolation
+          pytest -s tests/model/test_transformer.py
+      - name: Running FSDP rmpad model tests on 8 L20 GPUs + latest flash_attn
+        run: |
+          pip3 install hf_transfer
+          torchrun --nproc_per_node=8 tests/checkpoint/test_fsdp_ckpt.py
+      - name: Running transformers ulysses tests on 8 L20 GPUs + latest transformers
+        run: |
+          torchrun --nproc_per_node=8 -m pytest tests/model/test_transformers_ulysses.py
+      - name: Running transformers ulysses tests on 8 L20 GPUs + transformers 4.49.0
+        run: |
+          pip3 install transformers==4.49.0
+          torchrun --nproc_per_node=8 -m pytest tests/model/test_transformers_ulysses.py
+      - name: Running transformers ulysses tests on 8 L20 GPUs + transformers 4.48.0
+        run: |
+          pip3 install transformers==4.48.0
+          torchrun --nproc_per_node=8 -m pytest tests/model/test_transformers_ulysses.py
+      - name: Running transformers ulysses tests on 8 L20 GPUs + transformers 4.47.0
+        run: |
+          pip3 install transformers==4.47.0
+          torchrun --nproc_per_node=8 -m pytest tests/model/test_transformers_ulysses.py
+      - name: Running transformers ulysses tests on 8 L20 GPUs + transformers 4.46.0
+        run: |
+          pip3 install transformers==4.46.0
+          torchrun --nproc_per_node=8 -m pytest tests/model/test_transformers_ulysses.py
+      - name: Running transformers ulysses tests on 8 L20 GPUs + transformers 4.45.0
+        run: |
+          pip3 install transformers==4.45.0
+          torchrun --nproc_per_node=8 -m pytest tests/model/test_transformers_ulysses.py
+      - name: Run distributed test
+        run: |
+          bash tests/distributed/run_all.sh

.github/workflows/pylint.yml

@@ -0,0 +1,40 @@
+name: Pylint Check
+
+on:
+  push:
+    paths:
+      - '**.py'
+      - 'requirements.txt'
+      - 'pyproject.toml'
+  pull_request:
+    paths:
+      - '**.py'
+      - 'requirements.txt'
+      - 'pyproject.toml'
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'
+
+      - name: Install pylint (version from requirements.txt)
+        run: |
+          PYLINT_VERSION=$(grep '^pylint' requirements.txt)
+          if [ -z "$PYLINT_VERSION" ]; then
+            echo "No pylint version found in requirements.txt"
+            exit 1
+          fi
+          # only install pylint to avoid dependency problems on CPU
+          pip install "$PYLINT_VERSION"
+
+      - name: Run pylint
+        run: |
+          pylint --recursive=y --rcfile=pyproject.toml ./

.github/workflows/ray_test.yml

@@ -0,0 +1,55 @@
+name: ray
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "verl/single_controller/*.py"
+      - .github/workflows/ray_test.yml
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "verl/single_controller/*.py"
+      - .github/workflows/ray_test.yml
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  ray:
+    runs-on: [self-hosted, l20-0]
+    timeout-minutes: 5 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip install hf_transfer
+          pip install -e .[test]
+          pip install --upgrade "ray>=2.40.0"
+      - name: Running ray tests that need 8 GPUs
+        run: |
+          cd tests/ray
+          pytest -s -x --ignore=test_check_worker_alive.py --ignore=test_rvdz.py .

.github/workflows/sandbox.yml

@@ -0,0 +1,47 @@
+name: sandbox
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.3.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/sandbox.yml
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  sandbox:
+    runs-on: [self-hosted, l20-0]
+    timeout-minutes: 3 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test,prime]
+          pip3 install vllm==0.5.4
+      - name: Running sandbox tests on 8 L20 GPUs
+        run: |
+          cd tests/sandbox
+          pytest -s -x .

.github/workflows/sanity.yml

@@ -0,0 +1,54 @@
+name: sanity
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/sanity.yml
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/sanity.yml
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  sanity:
+    runs-on: ubuntu-latest
+    timeout-minutes: 5 # Increase this timeout value as needed
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install the current repository
+        run: |
+          pip install -e .[test]
+      - name: Run sanity test
+        run: |
+          pytest -s -x tests/sanity
+      - name: Run utility test
+        run: |
+          pytest -s -x tests/utility
+      - name: Run license test
+        run: |
+          python3 tests/sanity/check_license.py --directory .

.github/workflows/scorecard.yml

@@ -0,0 +1,64 @@
+# This workflow uses actions that are not certified by GitHub. They are provided
+# by a third-party and are governed by separate terms of service, privacy
+# policy, and support documentation.
+
+name: Scorecard supply-chain security
+on:
+  # For Branch-Protection check. Only the default branch is supported. See
+  # https://github.com/ossf/scorecard/blob/main/docs/checks.md#branch-protection
+  branch_protection_rule:
+  # To guarantee Maintained check is occasionally updated. See
+  # https://github.com/ossf/scorecard/blob/main/docs/checks.md#maintained
+  schedule:
+    - cron: '27 7 * * 1'
+  push:
+    branches: [ "main" ]
+
+# Declare default permissions as read only.
+permissions: read-all
+
+jobs:
+  analysis:
+    name: Scorecard analysis
+    runs-on: ubuntu-latest
+    permissions:
+      # Needed to upload the results to code-scanning dashboard.
+      security-events: write
+      # Needed to publish results and get a badge (see publish_results below).
+      id-token: write
+      # Uncomment the permissions below if installing in a private repository.
+      # contents: read
+      # actions: read
+
+    steps:
+      - name: "Checkout code"
+        uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
+        with:
+          persist-credentials: false
+
+      - name: "Run analysis"
+        uses: ossf/scorecard-action@0864cf19026789058feabb7e87baa5f140aac736 # v2.3.1
+        with:
+          results_file: results.sarif
+          results_format: sarif
+          # (Optional) "write" PAT token. Uncomment the `repo_token` line below if:
+          # - you want to enable the Branch-Protection check on a *public* repository, or
+          # - you are installing Scorecard on a *private* repository
+          # To create the PAT, follow the steps in https://github.com/ossf/scorecard-action?tab=readme-ov-file#authentication-with-fine-grained-pat-optional.
+          # repo_token: ${{ secrets.SCORECARD_TOKEN }}
+
+          # Public repositories:
+          #   - Publish results to OpenSSF REST API for easy access by consumers
+          #   - Allows the repository to include the Scorecard badge.
+          #   - See https://github.com/ossf/scorecard-action#publishing-results.
+          # For private repositories:
+          #   - `publish_results` will always be set to `false`, regardless
+          #     of the value entered here.
+          publish_results: true
+
+      # Upload the results to GitHub's code scanning dashboard (optional).
+      # Commenting out will disable upload of results to your repo's Code Scanning dashboard
+      - name: "Upload to code-scanning"
+        uses: github/codeql-action/upload-sarif@9e8d0789d4a0fa9ceb6b1738f7e269594bdd67f0 #v3.28.9
+        with:
+          sarif_file: results.sarif

.github/workflows/secrets_scan.yml

@@ -0,0 +1,21 @@
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+permissions:
+  contents: read
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
+      with:
+        fetch-depth: 0
+    - name: Secret Scanning
+      uses: trufflesecurity/trufflehog@7dc056a193116ba8d82154bf0549381c8fb8545c # v3.88.14
+      with:
+        extra_args: --results=verified,unknown

.github/workflows/vllm.yml

@@ -0,0 +1,64 @@
+name: vllm
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  pull_request:
+    branches:
+      - main
+      - v0.3.x
+    paths:
+      - "**/*.py"
+      - "verl/trainer/config/*.yaml"
+      - .github/workflows/vllm.yml
+      - "!recipe/**"
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  vllm:
+    runs-on: [self-hosted, l20-0]
+    timeout-minutes: 20 # Increase this timeout value as needed
+    env:
+      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
+      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
+      NO_PROXY: "localhost,127.0.0.1"
+      HF_HUB_ENABLE_HF_TRANSFER: 1
+    container:
+      image: verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+      options: --gpus all --shm-size=10g
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+            fetch-depth: 0
+      - name: Install the current repository
+        run: |
+          pip3 install hf_transfer
+          pip3 install -e .[test]
+          pip3 install vllm==0.5.4
+      - name: Running vllm tests on 8 L20 GPUs
+        run: |
+          cd tests/rollout
+          torchrun --standalone --nnodes=1 --nproc_per_node=8 $(which pytest) -s test_vllm_hf_loader.py
+      - name: Test the latest vLLM
+        run: |
+          pip3 install --upgrade vllm==0.7.3
+          cd tests/rollout
+          torchrun --standalone --nnodes=1 --nproc_per_node=4 $(which pytest) -s test_vllm_spmd.py
+      - name: Run Qwen 0.5B generation test
+        run: |
+          cd tests/generation
+          bash ./run_gen_qwen05.sh 4 $HOME/data/gen/qwen_05_gen_test.parquet 2
+          rm -rf $HOME/data/gen/qwen_05_gen_test.parquet
+      - name: Run Qwen 0.5B generation test when world_size == 1
+        run: |
+          cd tests/generation
+          bash ./run_gen_qwen05.sh 1 $HOME/data/gen/qwen_05_gen_test.parquet 1
+          rm -rf $HOME/data/gen/qwen_05_gen_test.parquet

.github/workflows/yapf_format.yml

@@ -0,0 +1,56 @@
+name: yapf
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/yapf_format.yml
+  pull_request:
+    branches:
+      - main
+      - v0.2.x
+    paths:
+      - "**/*.py"
+      - .github/workflows/yapf_format.yml
+
+# Cancel jobs on the same ref if a new one is triggered
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+# Declare permissions just read content.
+permissions: 
+  contents: read
+
+jobs:
+  yapf:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.12"]
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+      # - name: checkout
+      #   run: |
+      #     commits=${{ github.event.pull_request.commits }}
+      #     if [[ -n "$commits" ]]; then
+      #       # Prepare enough depth for diffs with main
+      #       git fetch --depth="$(( commits + 1 ))"
+      #     fi
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install --upgrade yapf
+          pip install toml==0.10.2
+      - name: Running yapf
+        run: |
+          yapf -r -vv -d --style=./.style.yapf verl tests examples recipe

.gitignore

@@ -0,0 +1,128 @@
+
+**/*.pt
+**/checkpoints
+**/wget-log
+**/_build/
+**/*.ckpt
+**/outputs
+**/*.tar.gz
+**/playground
+**/wandb
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+dataset/*
+tensorflow/my_graph/*
+.idea/
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+tmp/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+.venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# vscode
+.vscode
+
+# Mac
+.DS_Store
+
+# output logs
+tests/e2e/toy_examples/deepspeed/synchronous/output.txt
+
+# vim
+*.swp
+
+# ckpt
+*.lock
+
+# data
+*.parquet
+
+
+# local logs
+logs
+log
+outputs

.readthedocs.yaml

@@ -0,0 +1,19 @@
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+    rust: "1.70"
+
+sphinx:
+  configuration: docs/conf.py
+
+python:
+  install:
+    - requirements: docs/requirements-docs.txt
+    - method: pip
+      path: .

.style.yapf

@@ -0,0 +1,5 @@
+[style]
+based_on_style = google
+column_limit = 120
+indent_width = 4
+split_arguments_when_comma_terminated: true

LICENSE

@@ -0,0 +1,202 @@
+
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Notice.txt

@@ -0,0 +1 @@
+Copyright 2023-2024 Bytedance Ltd. and/or its affiliates 

README.md

@@ -0,0 +1,228 @@
+<h1 style="text-align: center;">AdaRFT: Adaptive Curriculum Reinforcement Finetuning</h1>
+
+📢 **New extension to `verl`!** We propose an adaptive curriculum learning method for efficient and scalable reinforcement finetuning (RFT) of LLMs — now implemented as an extension to this repo.
+
+> **Efficient Reinforcement Finetuning via Adaptive Curriculum Learning**  
+> Taiwei Shi†, Yiyang Wu†, Linxin Song†, Tianyi Zhou▽, Jieyu Zhao†  
+> †University of Southern California, ▽University of Maryland  
+> [[Paper]](https://arxiv.org/abs/2504.05520)
+
+### 🧠 Highlights
+- Dynamically adapts training difficulty using a lightweight curriculum scheduler
+- Compatible with standard RFT algorithms like PPO, GRPO, REINFORCE++
+- Improves both **sample efficiency** and **final accuracy** on math reasoning benchmarks
+- Up to **2× faster convergence** vs PPO baseline
+- Seamlessly integrated into `verl` without modifying reward functions or model architectures
+
+### 📦 Preprocessed Data
+- **Difficulty annotations**: [DeepScaleR](https://huggingface.co/datasets/lime-nlp/DeepScaleR_Difficulty)
+- **Training data**: [verl/data](https://github.com/uscnlp-lime/verl/tree/main/verl/data)
+
+### 🚀 Usage
+To use AdaRFT, you can simply use our example [script](https://github.com/uscnlp-lime/verl/blob/main/examples/adarft/run_qwen2.5-1.5b_seq_balance.sh). 
+
+You can also enable it in [ppo_trainer.yaml](https://github.com/uscnlp-lime/verl/blob/main/verl/trainer/config/ppo_trainer.yaml#L18-L24) or via command-line by setting the following flags:
+
+```bash
+python3 -m verl.trainer.main_ppo \
+    ... \
+    data.adarft.enable=True \
+    data.adarft.beta=0.5 \       # Target reward (success rate) the model aims to maintain
+    data.adarft.alpha=2 \        # Sensitivity of difficulty updates based on reward difference
+    data.adarft.eta=50 \         # Step size to scale reward signal to difficulty space
+    data.adarft.d_min=0 \        # Minimum difficulty bound
+    data.adarft.d_max=100 \      # Maximum difficulty bound
+    ...
+```
+
+Make sure your dataset includes difficulty scores (e.g., from [here](https://github.com/uscnlp-lime/verl/tree/main/verl/data)) for AdaRFT to function properly.
+
+### 📚 Citation
+✉️ Feel free to reach out to **Taiwei Shi ([email protected])** or **Jieyu Zhao ([email protected])** with questions or collaborations!
+
+```bibtex
+@misc{shi2025efficient,
+    title={Efficient Reinforcement Finetuning via Adaptive Curriculum Learning},
+    author={Taiwei Shi and Yiyang Wu and Linxin Song and Tianyi Zhou and Jieyu Zhao},
+    year={2025},
+    eprint={2504.05520},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+---
+
+<h1 style="text-align: center;">verl: Volcano Engine Reinforcement Learning for LLMs</h1>
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/volcengine/verl)](https://github.com/volcengine/verl/stargazers)
+![GitHub forks](https://img.shields.io/github/forks/volcengine/verl)
+[![Twitter](https://img.shields.io/twitter/follow/verl_project)](https://twitter.com/verl_project)
+<a href="https://join.slack.com/t/verlgroup/shared_invite/zt-2w5p9o4c3-yy0x2Q56s_VlGLsJ93A6vA"><img src="https://img.shields.io/badge/Slack-verl-blueviolet?logo=slack&amp"></a>
+<a href="https://arxiv.org/pdf/2409.19256"><img src="https://img.shields.io/static/v1?label=EuroSys&message=Paper&color=red"></a>
+![GitHub contributors](https://img.shields.io/github/contributors/volcengine/verl)
+[![Documentation](https://img.shields.io/badge/documentation-blue)](https://verl.readthedocs.io/en/latest/)
+<a href="https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/WeChat.JPG"><img src="https://img.shields.io/badge/微信-green?logo=wechat&amp"></a>
+
+
+verl is a flexible, efficient and production-ready RL training library for large language models (LLMs).
+
+verl is the open-source version of **[HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2)** paper.
+
+verl is flexible and easy to use with:
+
+- **Easy extension of diverse RL algorithms**: The hybrid-controller programming model enables flexible representation and efficient execution of complex Post-Training dataflows. Build RL dataflows such as GRPO, PPO in a few lines of code.
+
+- **Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as FSDP, Megatron-LM, vLLM, SGLang, etc
+
+- **Flexible device mapping**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes.
+
+- Ready integration with popular HuggingFace models
+
+
+verl is fast with:
+
+- **State-of-the-art throughput**: SOTA LLM training and inference engine integrations and SOTA RL throughput.
+
+- **Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases.
+
+</p>
+
+## News
+- [2025/03] verl v0.3.0.post1 is released! See [release note](https://github.com/volcengine/verl/releases/) for details.
+- [2025/03] [DAPO](https://dapo-sia.github.io/) is the open-sourced SOTA RL algorithm that achieves 50 points on AIME 2024 based on the Qwen2.5-32B pre-trained model, surpassing the previous SOTA achieved by DeepSeek's GRPO (DeepSeek-R1-Zero-Qwen-32B). DAPO's training is fully powered by verl and the reproduction code is [publicly available](https://github.com/volcengine/verl/tree/gm-tyx/puffin/main/recipe/dapo) now.
+- [2025/03] We will present verl(HybridFlow) at EuroSys 2025. See you in Rotterdam!
+- [2025/03] We introduced the programming model of verl at the [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg) and [verl intro and updates](https://github.com/eric-haibin-lin/verl-community/blob/main/slides/verl-lmsys-meetup.pdf) at the [LMSys Meetup](https://lu.ma/ntjrr7ig) in Sunnyvale mid March.
+- [2025/02] verl v0.2.0.post2 is released!
+- [2025/01] [Doubao-1.5-pro](https://team.doubao.com/zh/special/doubao_1_5_pro) is released with SOTA-level performance on LLM & VLM. The RL scaling preview model is trained using verl, reaching OpenAI O1-level performance on math benchmarks (70.0 pass@1 on AIME).
+<details><summary> more... </summary>
+<ul>
+  <li>[2025/02] We presented verl in the <a href="https://lu.ma/ji7atxux">Bytedance/NVIDIA/Anyscale Ray Meetup</a>. See you in San Jose!</li>
+  <li>[2024/12] verl is presented at Ray Forward 2024. Slides available <a href="https://github.com/eric-haibin-lin/verl-community/blob/main/slides/Ray_Forward_2024_%E5%B7%AB%E9%94%A1%E6%96%8C.pdf">here</a></li>
+  <li>[2024/10] verl is presented at Ray Summit. <a href="https://www.youtube.com/watch?v=MrhMcXkXvJU&list=PLzTswPQNepXntmT8jr9WaNfqQ60QwW7-U&index=37">Youtube video</a> available.</li>
+  <li>[2024/12] The team presented <a href="https://neurips.cc/Expo/Conferences/2024/workshop/100677">Post-training LLMs: From Algorithms to Infrastructure</a> at NeurIPS 2024. <a href="https://github.com/eric-haibin-lin/verl-data/tree/neurips">Slides</a> and <a href="https://neurips.cc/Expo/Conferences/2024/workshop/100677">video</a> available.</li>
+  <li>[2024/08] HybridFlow (verl) is accepted to EuroSys 2025.</li>
+</ul>   
+</details>
+
+## Key Features
+
+- **FSDP** and **Megatron-LM** for training.
+- **vLLM**, **SGLang**(experimental) and **HF Transformers** for rollout generation.
+- Compatible with Hugging Face Transformers and Modelscope Hub: Qwen-2.5, Llama3.1, Gemma2, DeepSeek-LLM, etc
+- Supervised fine-tuning.
+- Reinforcement learning with [PPO](examples/ppo_trainer/), [GRPO](examples/grpo_trainer/), [ReMax](examples/remax_trainer/), [REINFORCE++](https://verl.readthedocs.io/en/latest/examples/config.html#algorithm), [RLOO](examples/rloo_trainer/), [PRIME](recipe/prime/), etc.
+  - Support model-based reward and function-based reward (verifiable reward)
+  - Support vision-language models (VLMs) and [multi-modal RL](examples/grpo_trainer/run_qwen2_5_vl-7b.sh)
+- Flash attention 2, [sequence packing](examples/ppo_trainer/run_qwen2-7b_seq_balance.sh), [sequence parallelism](examples/ppo_trainer/run_deepseek7b_llm_sp2.sh) support via DeepSpeed Ulysses, [LoRA](examples/sft/gsm8k/run_qwen_05_peft.sh), [Liger-kernel](examples/sft/gsm8k/run_qwen_05_sp2_liger.sh).
+- Scales up to 70B models and hundreds of GPUs.
+- Experiment tracking with wandb, swanlab, mlflow and tensorboard.
+
+## Upcoming Features
+- Roadmap https://github.com/volcengine/verl/issues/710 
+- DeepSeek 671b optimizations with Megatron v0.11 https://github.com/volcengine/verl/issues/708 
+- Multi-turn rollout optimizations
+- Environment interactions
+
+## Getting Started
+
+<a href="https://verl.readthedocs.io/en/latest/index.html"><b>Documentation</b></a>
+
+**Quickstart:**
+- [Installation](https://verl.readthedocs.io/en/latest/start/install.html)
+- [Quickstart](https://verl.readthedocs.io/en/latest/start/quickstart.html)
+- [Programming Guide](https://verl.readthedocs.io/en/latest/hybrid_flow.html)
+
+**Running a PPO example step-by-step:**
+- Data and Reward Preparation
+  - [Prepare Data for Post-Training](https://verl.readthedocs.io/en/latest/preparation/prepare_data.html)
+  - [Implement Reward Function for Dataset](https://verl.readthedocs.io/en/latest/preparation/reward_function.html)
+- Understanding the PPO Example
+  - [PPO Example Architecture](https://verl.readthedocs.io/en/latest/examples/ppo_code_architecture.html)
+  - [Config Explanation](https://verl.readthedocs.io/en/latest/examples/config.html)
+  - [Run GSM8K Example](https://verl.readthedocs.io/en/latest/examples/gsm8k_example.html)
+
+**Reproducible algorithm baselines:**
+- [PPO, GRPO, ReMax](https://verl.readthedocs.io/en/latest/experiment/ppo.html)
+
+**For code explanation and advance usage (extension):**
+- PPO Trainer and Workers
+  - [PPO Ray Trainer](https://verl.readthedocs.io/en/latest/workers/ray_trainer.html)
+  - [PyTorch FSDP Backend](https://verl.readthedocs.io/en/latest/workers/fsdp_workers.html)
+  - [Megatron-LM Backend](https://verl.readthedocs.io/en/latest/index.html)
+- Advance Usage and Extension
+  - [Ray API design tutorial](https://verl.readthedocs.io/en/latest/advance/placement.html)
+  - [Extend to Other RL(HF) algorithms](https://verl.readthedocs.io/en/latest/advance/dpo_extension.html)
+  - [Add Models with the FSDP Backend](https://verl.readthedocs.io/en/latest/advance/fsdp_extension.html)
+  - [Add Models with the Megatron-LM Backend](https://verl.readthedocs.io/en/latest/advance/megatron_extension.html)
+  - [Deployment using Separate GPU Resources](https://github.com/volcengine/verl/tree/main/examples/split_placement)
+
+**Blogs from the community**
+- [使用verl进行GRPO分布式强化学习训练最佳实践](https://www.volcengine.com/docs/6459/1463942)
+- [HybridFlow veRL 原文浅析](https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/blob/main/rlhf/verl/readme.md)
+- [最高提升20倍吞吐量！豆包大模型团队发布全新 RLHF 框架，现已开源！](https://team.doubao.com/en/blog/%E6%9C%80%E9%AB%98%E6%8F%90%E5%8D%8720%E5%80%8D%E5%90%9E%E5%90%90%E9%87%8F-%E8%B1%86%E5%8C%85%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%9B%A2%E9%98%9F%E5%8F%91%E5%B8%83%E5%85%A8%E6%96%B0-rlhf-%E6%A1%86%E6%9E%B6-%E7%8E%B0%E5%B7%B2%E5%BC%80%E6%BA%90)
+
+
+## Performance Tuning Guide
+The performance is essential for on-policy RL algorithm. We have written a detailed [performance tuning guide](https://verl.readthedocs.io/en/latest/perf/perf_tuning.html) to help you optimize performance.
+
+## Use vLLM v0.8.2
+veRL now supports vLLM>=0.8.2 when using FSDP as the training backend. Please refer to [this document](https://github.com/volcengine/verl/blob/main/docs/README_vllm0.8.md) for installation guide and more information. Please avoid vllm 0.7.x which contains bugs that may lead to OOMs and unexpected errors.
+
+## Citation and acknowledgement
+
+If you find the project helpful, please cite:
+- [HybridFlow: A Flexible and Efficient RLHF Framework](https://arxiv.org/abs/2409.19256v2)
+- [A Framework for Training Large Language Models for Code Generation via Proximal Policy Optimization](https://i.cs.hku.hk/~cwu/papers/gmsheng-NL2Code24.pdf)
+
+```bibtex
+@article{sheng2024hybridflow,
+  title   = {HybridFlow: A Flexible and Efficient RLHF Framework},
+  author  = {Guangming Sheng and Chi Zhang and Zilingfeng Ye and Xibin Wu and Wang Zhang and Ru Zhang and Yanghua Peng and Haibin Lin and Chuan Wu},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: 2409.19256}
+}
+```
+
+verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The project is adopted and contributed by Bytedance, Anyscale, LMSys.org, [Alibaba Qwen team](https://github.com/QwenLM/), Shanghai AI Lab, Tsinghua University, UC Berkeley, UCLA, UIUC, University of Hong Kong, ke.com, [All Hands AI](https://www.all-hands.dev/), [ModelBest](http://modelbest.cn/), [OpenPipe](https://openpipe.ai/), JD AI Lab, Microsoft Research, [StepFun](https://www.stepfun.com/), Amazon, Linkedin, Meituan, [Camel-AI](https://www.camel-ai.org/), [OpenManus](https://github.com/OpenManus), [Prime Intellect](https://www.primeintellect.ai/), NVIDIA research, [Baichuan](https://www.baichuan-ai.com/home), and many more.
+
+## Awesome work using verl
+- [TinyZero](https://github.com/Jiayi-Pan/TinyZero): a reproduction of **DeepSeek R1 Zero** recipe for reasoning tasks ![GitHub Repo stars](https://img.shields.io/github/stars/Jiayi-Pan/TinyZero)
+- [DAPO](https://dapo-sia.github.io/): the fully open source SOTA RL algorithm that beats DeepSeek-R1-zero-32B ![GitHub Repo stars](https://img.shields.io/github/stars/volcengine/verl)
+- [SkyThought](https://github.com/NovaSky-AI/SkyThought): RL training for Sky-T1-7B by NovaSky AI team. ![GitHub Repo stars](https://img.shields.io/github/stars/NovaSky-AI/SkyThought)
+- [simpleRL-reason](https://github.com/hkust-nlp/simpleRL-reason): SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild ![GitHub Repo stars](https://img.shields.io/github/stars/hkust-nlp/simpleRL-reason)
+- [Easy-R1](https://github.com/hiyouga/EasyR1): **Multi-modal** RL training framework ![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/EasyR1)
+- [OpenManus-RL](https://github.com/OpenManus/OpenManus-RL): LLM Agents RL tunning framework for multiple agent environments. ![GitHub Repo stars](https://img.shields.io/github/stars/OpenManus/OpenManus-RL)
+- [deepscaler](https://github.com/agentica-project/deepscaler): iterative context scaling with GRPO ![GitHub Repo stars](https://img.shields.io/github/stars/agentica-project/deepscaler)
+- [PRIME](https://github.com/PRIME-RL/PRIME): Process reinforcement through implicit rewards ![GitHub Repo stars](https://img.shields.io/github/stars/PRIME-RL/PRIME)
+- [RAGEN](https://github.com/ZihanWang314/ragen): a general-purpose reasoning **agent** training framework ![GitHub Repo stars](https://img.shields.io/github/stars/ZihanWang314/ragen)
+- [Logic-RL](https://github.com/Unakar/Logic-RL): a reproduction of DeepSeek R1 Zero on 2K Tiny Logic Puzzle Dataset. ![GitHub Repo stars](https://img.shields.io/github/stars/Unakar/Logic-RL)
+- [Search-R1](https://github.com/PeterGriffinJin/Search-R1): RL with reasoning and **searching (tool-call)** interleaved LLMs ![GitHub Repo stars](https://img.shields.io/github/stars/PeterGriffinJin/Search-R1)
+- [ReSearch](https://github.com/Agent-RL/ReSearch): Learning to **Re**ason with **Search** for LLMs via Reinforcement Learning ![GitHub Repo stars](https://img.shields.io/github/stars/Agent-RL/ReSearch)
+- [DeepRetrieval](https://github.com/pat-jj/DeepRetrieval): Hacking **Real Search Engines** and **retrievers** with LLMs via RL for **information retrieval** ![GitHub Repo stars](https://img.shields.io/github/stars/pat-jj/DeepRetrieval)
+- [cognitive-behaviors](https://github.com/kanishkg/cognitive-behaviors): Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs ![GitHub Repo stars](https://img.shields.io/github/stars/kanishkg/cognitive-behaviors)
+- [PURE](https://github.com/CJReinforce/PURE): **Credit assignment** is the key to successful reinforcement fine-tuning using **process reward model** ![GitHub Repo stars](https://img.shields.io/github/stars/CJReinforce/PURE)
+- [MetaSpatial](https://github.com/PzySeere/MetaSpatial): Reinforcing **3D Spatial Reasoning** in **VLMs** for the **Metaverse** ![GitHub Repo stars](https://img.shields.io/github/stars/PzySeere/MetaSpatial)
+- [DeepEnlighten](https://github.com/DolbyUUU/DeepEnlighten): Reproduce R1 with **social reasoning** tasks and analyze key findings ![GitHub Repo stars](https://img.shields.io/github/stars/DolbyUUU/DeepEnlighten)
+- [Code-R1](https://github.com/ganler/code-r1): Reproducing R1 for **Code** with Reliable Rewards ![GitHub Repo stars](https://img.shields.io/github/stars/ganler/code-r1)
+- [DeepResearcher](https://github.com/GAIR-NLP/DeepResearcher): Scaling deep research via reinforcement learning in real-world environments ![GitHub Repo stars](https://img.shields.io/github/stars/GAIR-NLP/DeepResearcher)
+- [self-rewarding-reasoning-LLM](https://arxiv.org/pdf/2502.19613): self-rewarding and correction with **generative reward models** ![GitHub Repo stars](https://img.shields.io/github/stars/RLHFlow/Self-rewarding-reasoning-LLM)
+- [critic-rl](https://github.com/HKUNLP/critic-rl): LLM critics for code generation ![GitHub Repo stars](https://img.shields.io/github/stars/HKUNLP/critic-rl)
+- [DQO](https://arxiv.org/abs/2410.09302): Enhancing multi-Step reasoning abilities of language models through direct Q-function optimization
+- [FIRE](https://arxiv.org/abs/2410.21236): Flaming-hot initiation with regular execution sampling for large language models
+- [Rec-R1](https://arxiv.org/pdf/2503.24289): Bridging Generative Large Language Models and Recommendation Systems via Reinforcement Learning
+
+
+## Contribution Guide
+Contributions from the community are welcome! Please check out our [project roadmap](https://github.com/volcengine/verl/issues/710) and [good first issues](https://github.com/volcengine/verl/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22) to see where you can contribute.
+
+### Code formatting
+We use yapf (Google style) to enforce strict code formatting when reviewing PRs. To reformat your code locally, make sure you have installed the **latest** version of `yapf`
+```bash
+pip3 install yapf --upgrade
+```
+Then, make sure you are at top level of verl repo and run
+```bash
+bash scripts/format.sh
+```
+We are HIRING! Send us an [email](mailto:[email protected]) if you are interested in internship/FTE opportunities in MLSys/LLM reasoning/multimodal alignment.

docker/Dockerfile.megatron

@@ -0,0 +1,9 @@
+FROM verlai/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te1.7-v0.0.3
+
+RUN pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable
+
+RUN cd /opt/nvidia && git clone --single-branch --branch core_r0.11.0 https://github.com/NVIDIA/Megatron-LM.git Megatron-LM
+
+# only config pip index with https://pypi.tuna.tsinghua.edu.cn/simple if needed
+# unset for now
+RUN cd /opt/nvidia/Megatron-LM && pip3 install --no-deps -e .

docker/Dockerfile.ngc.vllm

@@ -0,0 +1,47 @@
+# docker buildx build --platform linux/x86_64 -t "verlai/verl:ngc-th2.4.0-cu124-vllm0.6.3-ray2.4-te1.7-v0.0.6" -f docker/Dockerfile.ngc.vllm . --builder cloud-verlai-verl-builder --progress=plain --push
+FROM nvcr.io/nvidia/pytorch:24.05-py3
+
+# uninstall nv-pytorch fork
+RUN pip3 uninstall pytorch-quantization \
+     pytorch-triton \
+     torch \
+     torch-tensorrt \
+     torchvision \
+     xgboost transformer_engine flash_attn \
+     apex megatron-core -y
+
+RUN pip3 install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124
+
+# =============== Megatron dependencies (optional) =================
+# install apex, set MAX_JOBS to avoid OOMs
+RUN MAX_JOBS=4 pip3 install -v --disable-pip-version-check --no-cache-dir --no-build-isolation \
+    --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" \
+    git+https://github.com/NVIDIA/apex
+# =============== End of Megatron dependencies (optional) =================
+
+RUN pip3 install --no-cache-dir \
+    accelerate \
+    codetiming \
+    datasets \
+    dill \
+    hydra-core \
+    numpy \
+    'pandas' \
+    'peft' \
+    'pyarrow>=15.0.0' \
+    'pybind11' \
+    'pylatexenc' \
+    'ray>=2.10' \
+    'tensordict<0.6' \
+    'transformers' \
+    'vllm==0.6.3.post1' \
+    'wandb'
+
+# full dependencies
+RUN pip3 install pytest yapf py-spy pyext liger-kernel
+
+# =============== Megatron dependencies (optional) =================
+# install Transformer Engine, which requires FA 2.5.8. Do it in a separate step for docker cache
+RUN MAX_JOBS=4 NINJA_FLAGS="-j4" pip3 install flash-attn==2.5.8 --no-cache-dir --no-build-isolation
+RUN MAX_JOBS=1 NINJA_FLAGS="-j1" TE_BUILD_WITH_NINJA=0 pip3 install git+https://github.com/eric-haibin-lin/[email protected]
+# =============== End of Megatron dependencies (optional) =================

docker/Dockerfile.ngc.vllm0.8

@@ -0,0 +1,66 @@
+# Start from the NVIDIA official image (ubuntu-22.04 + python-3.10)
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html
+FROM nvcr.io/nvidia/pytorch:24.08-py3
+
+# uninstall nv-pytorch fork
+RUN pip3 uninstall -y pytorch-quantization \
+    pytorch-triton torch torch-tensorrt torchvision \
+    xgboost transformer_engine flash_attn apex megatron-core
+
+# Define environments
+ENV MAX_JOBS=32
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
+ENV DEBIAN_FRONTEND=noninteractive
+ENV NODE_OPTIONS=""
+ENV HF_HUB_ENABLE_HF_TRANSFER="1"
+
+# Define installation arguments
+ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/
+ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+
+# Set apt source
+RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \
+    { \
+    echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \
+    } > /etc/apt/sources.list
+
+# Install systemctl
+RUN apt-get update && \
+    apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \
+    apt-get clean
+
+# Install tini
+RUN apt-get update && \
+    apt-get install -y tini && \
+    apt-get clean
+
+# Change pip source
+RUN pip config set global.index-url "${PIP_INDEX}" && \
+    pip config set global.extra-index-url "${PIP_INDEX}" && \
+    python -m pip install --upgrade pip
+
+# Install torch-2.6.0 + vllm-0.8.2
+RUN pip install --no-cache-dir vllm==0.8.2 torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 tensordict torchdata \
+    transformers>=4.49.0 accelerate datasets peft hf-transfer \
+    ray[default] codetiming hydra-core pandas pyarrow>=15.0.0 pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler \
+    pytest yapf py-spy pyext pre-commit ruff
+
+# Install flash_attn-2.7.4.post1
+RUN pip uninstall -y transformer-engine flash-attn && \
+    wget -nv https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl && \
+    pip install --no-cache-dir flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+
+# Fix cv2
+RUN pip uninstall -y pynvml nvidia-ml-py && \
+    pip install --no-cache-dir nvidia-ml-py>=12.560.30 opencv-python-headless==4.8.0.74 fastapi==0.115.6 && \
+    pip install --no-cache-dir --upgrade optree>=0.13.0
+
+# Install verl
+RUN pip install --no-cache-dir verl[vllm] -U
+
+# Reset pip config
+RUN pip config unset global.index-url && \
+    pip config unset global.extra-index-url

docker/Dockerfile.ngc.vllm0.8.sagemaker

@@ -0,0 +1,46 @@
+# Using a pre-built image from AWS DLC which contains the current version of python (3.10) and supported cuda version (12.1)
+FROM 763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:2.1.0-transformers4.36.0-gpu-py310-cu121-ubuntu20.04
+
+# uninstall nv-pytorch fork
+RUN pip3 uninstall -y pytorch-quantization \
+    pytorch-triton torch torch-tensorrt torchvision \
+    xgboost transformer_engine flash_attn apex megatron-core
+
+# Define environments
+ENV MAX_JOBS=32
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
+ENV DEBIAN_FRONTEND=noninteractive
+ENV NODE_OPTIONS=""
+ENV HF_HUB_ENABLE_HF_TRANSFER="1"
+
+# Install systemctl
+RUN apt-get update && \
+    apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \
+    apt-get clean
+
+# Install tini
+RUN apt-get update && \
+    apt-get install -y tini && \
+    apt-get clean
+
+# Install torch-2.6.0 + vllm-0.8.2
+RUN pip install --no-cache-dir vllm==0.8.2 torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 tensordict torchdata==0.11.0 \
+    transformers>=4.49.0 accelerate datasets peft hf-transfer \
+    ray[default] codetiming hydra-core pandas pyarrow>=15.0.0 pylatexenc qwen-vl-utils wandb dill pybind11 liger-kernel mathruler \
+    pytest yapf py-spy pyext pre-commit ruff
+
+# Install flash_attn-2.7.4.post1
+RUN pip uninstall -y transformer-engine flash-attn && \
+    pip install flash-attn==2.7.4.post1 --no-build-isolation
+
+# Fix cv2
+RUN pip uninstall -y pynvml nvidia-ml-py && \
+    pip install --no-cache-dir nvidia-ml-py>=12.560.30 opencv-python-headless==4.8.0.74 fastapi==0.115.6 && \
+    pip install --no-cache-dir --upgrade optree>=0.13.0
+
+# Install verl
+RUN pip install --no-cache-dir verl[vllm] -U
+
+# Reset pip config
+RUN pip config unset global.index-url && \
+    pip config unset global.extra-index-url

docker/Dockerfile.rocm

@@ -0,0 +1,45 @@
+#  Build the docker in the repo dir:
+# docker build -f docker/Dockerfile.rocm -t verl-rocm:03.04.2015 .
+# docker images # you can find your built docker
+
+
+FROM rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
+
+# Set working directory
+# WORKDIR $PWD/app
+
+# Set environment variables
+ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942"
+
+# Install vllm
+RUN pip uninstall -y vllm && \
+    rm -rf vllm && \
+    git clone -b v0.6.3 https://github.com/vllm-project/vllm.git && \
+    cd vllm && \
+    MAX_JOBS=$(nproc) python3 setup.py install && \
+    cd .. && \
+    rm -rf vllm
+
+# Copy the entire project directory
+COPY . .
+
+# Install dependencies
+RUN pip install "tensordict<0.6" --no-deps && \
+    pip install accelerate \
+    codetiming \
+    datasets \
+    dill \
+    hydra-core \
+    liger-kernel \
+    numpy \
+    pandas \
+    peft \
+    "pyarrow>=15.0.0" \
+    pylatexenc \
+    "ray[data,train,tune,serve]" \
+    torchdata \
+    transformers \
+    wandb \
+    orjson \
+    pybind11 && \
+    pip install -e . --no-deps

docker/Dockerfile.sglang

@@ -0,0 +1,55 @@
+# Start from the NVIDIA official image (ubuntu-22.04 + python-3.10)
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-08.html
+FROM nvcr.io/nvidia/pytorch:24.08-py3
+
+# Define environments
+ENV MAX_JOBS=32
+ENV DEBIAN_FRONTEND=noninteractive
+ENV NODE_OPTIONS=""
+
+# Define installation arguments
+ARG APT_SOURCE=https://mirrors.ustc.edu.cn/ubuntu/
+
+# Set apt source
+RUN cp /etc/apt/sources.list /etc/apt/sources.list.bak && \
+    { \
+    echo "deb ${APT_SOURCE} jammy main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse"; \
+    echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse"; \
+    } > /etc/apt/sources.list
+
+# Install systemctl
+RUN apt-get update && \
+    apt-get install -y -o Dpkg::Options::="--force-confdef" systemd && \
+    apt-get clean
+
+# Install tini
+RUN apt-get update && \
+    apt-get install -y tini && \
+    apt-get clean
+
+# Change pip source
+ARG PIP_INDEX=https://mirrors.aliyun.com/pypi/simple/
+
+RUN pip config set global.index-url "${PIP_INDEX}" && \
+    pip config set global.extra-index-url "${PIP_INDEX}" && \
+    python -m pip install --upgrade pip
+
+# Install sglang-0.4.4.post4 and torch-memory-saver
+RUN pip install "sglang[all]==0.4.4.post4" --no-cache-dir --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python && pip install torch-memory-saver --no-cache-dir
+
+# Install torch-2.5.1
+RUN pip install --no-cache-dir torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 tensordict torchdata \
+    transformers>=4.49.0 accelerate datasets peft hf_transfer \
+    ray codetiming hydra-core pandas pyarrow>=15.0.0 pylatexenc qwen-vl-utils wandb liger-kernel \
+    pytest yapf py-spy pyext
+
+# Install flash_attn-2.7.4.post1
+RUN pip uninstall -y transformer-engine flash-attn && \
+    wget -v https://ghfast.top/https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl && \
+    pip install --no-cache-dir flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+
+# Fix cv2
+RUN pip uninstall -y pynvml nvidia-ml-py && \
+    pip install --no-cache-dir nvidia-ml-py>=12.560.30 opencv-python-headless==4.8.0.74 fastapi==0.115.6

docker/Dockerfile.vemlp.vllm.te

@@ -0,0 +1,41 @@
+# docker buildx build --platform linux/x86_64 -t "verlai/verl:$TAG" -f docker/$FILE .
+
+# the one in docker.io is an alias for the one veturbo
+# FROM vemlp-cn-beijing.cr.volces.com/veturbo/pytorch:2.4-cu124
+FROM docker.io/haibinlin/verl:v0.0.5-th2.4.0-cu124-base
+
+# only config pip index with https://pypi.tuna.tsinghua.edu.cn/simple if needed
+# unset for now
+RUN pip3 config unset global.index-url
+
+# transformers 4.47.0 contains the following bug:
+# AttributeError: 'Gemma2Attention' object has no attribute '_flash_attn_uses_top_left_mask'
+RUN pip3 install --no-cache-dir \
+    torch==2.4.0 \
+    accelerate \
+    codetiming \
+    dill \
+    hydra-core \
+    numpy \
+    pybind11 \
+    tensordict \
+    "transformers <= 4.46.0"
+
+RUN pip3 install --no-cache-dir flash-attn==2.7.0.post2 --no-build-isolation
+
+# vllm depends on ray, and veRL does not support ray > 2.37
+RUN pip3 install --no-cache-dir vllm==0.6.3 ray==2.10
+
+# install apex
+RUN MAX_JOBS=4 pip3 install -v --disable-pip-version-check --no-cache-dir --no-build-isolation \
+    --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" \
+    git+https://github.com/NVIDIA/apex
+
+# install Transformer Engine
+# - flash-attn pinned to 2.5.3 by TransformerEngine, switch to eric-haibin-lin/[email protected] to relax version req
+# - install with: MAX_JOBS=1 NINJA_FLAGS="-j1" TE_BUILD_WITH_NINJA=0 to avoid OOM
+# - cudnn is required by TransformerEngine
+# RUN CUDNN_PATH=/opt/conda/lib/python3.11/site-packages/nvidia/cudnn \
+#     pip3 install git+https://github.com/eric-haibin-lin/[email protected]
+RUN MAX_JOBS=1 NINJA_FLAGS="-j1" pip3 install flash-attn==2.5.3 --no-cache-dir --no-build-isolation
+RUN MAX_JOBS=1 NINJA_FLAGS="-j1" pip3 install git+https://github.com/NVIDIA/[email protected]

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = verl
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/README.md

@@ -0,0 +1,19 @@
+# verl documents
+
+## Build the docs
+
+```bash
+# Install dependencies.
+pip install -r requirements-docs.txt
+
+# Build the docs.
+make clean
+make html
+```
+
+## Open the docs with your browser
+
+```bash
+python -m http.server -d _build/html/
+```
+Launch your browser and navigate to http://localhost:8000 to view the documentation.

docs/README_vllm0.7.md

@@ -0,0 +1,71 @@
+# Upgrading to vllm >= 0.7
+
+## Installation
+
+Note: This version of veRL+vllm 0.7+ supports **FSDP** for training and **vLLM** for rollout.
+
+```
+# Create the conda environment
+conda create -n verl python==3.10
+conda activate verl
+
+# Install verl
+git clone https://github.com/volcengine/verl.git
+cd verl
+pip3 install -e .
+
+# Install the latest stable version of vLLM
+pip3 install vllm==0.7.3 
+
+# Install flash-attn
+pip3 install flash-attn --no-build-isolation
+
+```
+
+Note that if you are installing lower versions of vLLM (0.7.0, 0.7.1, 0.7.2), you need to make some tiny patches manually on vllm (/path/to/site-packages/vllm after installation) after the above steps:
+
+- vllm/distributed/parallel_state.py: Remove the assertion below:
+
+```
+if (world_size
+        != tensor_model_parallel_size * pipeline_model_parallel_size):
+    raise RuntimeError(
+        f"world_size ({world_size}) is not equal to "
+        f"tensor_model_parallel_size ({tensor_model_parallel_size}) x "
+        f"pipeline_model_parallel_size ({pipeline_model_parallel_size})")
+
+```
+
+- vllm/executor/uniproc_executor.py: change `local_rank = rank` to `local_rank = int(os.environ["LOCAL_RANK"])`
+- vllm/model_executor/model_loader/weight_utils.py: remove the `torch.cuda.empty_cache()` in `pt_weights_iterator`
+
+## Features
+
+### Use cuda graph
+
+After installation, examples using FSDP as training backends can be used. By default, the `enforce_eager` is set to True, which disables the cuda graph. To enjoy cuda graphs and the sleep mode of vLLM>=0.7, add the following lines to the bash script:
+
+```
+actor_rollout_ref.rollout.enforce_eager=False \
+actor_rollout_ref.rollout.free_cache_engine=False \
+
+```
+
+For a typical job like examples/ppo_trainer/run_qwen2-7b_seq_balance.sh, the rollout generation time is 115 seconds with vLLM0.6.3, while it is 85 seconds with vLLM0.7.0. By enabling the cudagraph, the generation duration is further reduced to 62 seconds.
+
+**Note:** Currently, if the `n` is greater than 1 in `SamplingParams` in vLLM>=0.7, there is a potential performance issue on the stability of rollout generation time (Some iterations would see generation time bursts) using vLLM's V0 Engine.
+
+### Use vLLM V1 Engine
+
+Using the vLLM V1 engine can avoid instability issues and achieve additional performance improvements. To use the V1 engine, you can first uninstall the previously installed vLLM and then follow the steps below to install the newer version.
+
+```
+git clone https://github.com/vllm-project/vllm.git
+cd vllm
+git checkout 2275784
+sed -i "903a\    data_parallel_size = world_size // pipeline_model_parallel_size // tensor_model_parallel_size" ./vllm/distributed/parallel_state.py
+VLLM_USE_PRECOMPILED=1 pip install --editable .
+```
+
+Then you can enable the V1 engine by setting `export VLLM_USE_V1=1`. In some benchmark tests, the V1 engine demonstrates a 1.5x speed improvement over the vLLM V0 engine.
+The stable support of the vLLM V1 engine will come soon.

docs/README_vllm0.8.md

@@ -0,0 +1,54 @@
+# Upgrading to vLLM >= 0.8
+
+## Installation
+
+Note: This version of veRL+vLLM 0.8+ supports **FSDP** for training and **vLLM** for rollout.
+
+```bash
+# Create the conda environment
+conda create -n verl python==3.10
+conda activate verl
+
+# Install verl
+git clone https://github.com/volcengine/verl.git
+cd verl
+pip3 install -e .
+
+# Install the latest stable version of vLLM
+pip3 install vllm==0.8.2
+
+# Install flash-attn
+pip3 install flash-attn --no-build-isolation
+
+```
+
+We have a pre-built docker image for veRL+vLLM 0.8.2. You can direct import it with the following command:
+
+```bash
+docker pull hiyouga/verl:ngc-th2.6.0-cu120-vllm0.8.2
+```
+
+## Features
+
+vLLM 0.8+ supports cuda graph and V1 engine by default in veRL. To enable these features, remember to add the following lines to the bash script:
+
+```bash
+actor_rollout_ref.rollout.enforce_eager=False \
+actor_rollout_ref.rollout.free_cache_engine=False \
+```
+
+and also **remove** the environment variable if it exists:
+
+```bash
+export VLLM_ATTENTION_BACKEND=XFORMERS
+```
+
+## Notes
+
+When you just directly upgrade vllm>=0.8, some dependency packages may undergo version changes. If you encounter the following problems:
+
+```bash
+in <module> from torch.multiprocessing.reductions import ForkingPickler ImportError: cannot import name 'ForkingPickler' from 'torch.multiprocessing.reductions' (/opt/conda/lib/python3.11/site-packages/torch/multiprocessing/reductions.py)
+```
+
+You need to upgrade `tensordict` to version 0.6.2 using the command `pip install tensordict==0.6.2`.

docs/advance/checkpoint.rst

@@ -0,0 +1,122 @@
+Using Checkpoints to Support Fault Tolerance Training
+=====================================================
+
+There could be training errors or machine failure during the whole RLHF training process, 
+so it is recommended to enable checkpoints to minimize your loss.
+
+The API Interface has already been listed in :ref:`config-explain-page`,
+and we will not repeat them. But there are still some technique details
+we hope to clarify.
+
+.. note:: 
+
+    Notice that the ``checkpoint.contents`` field has no effect to FSDP checkpoint except ``hf_model``, 
+    the other 3 fields are binded together to save and load. We recommend to include ``model``, ``optimizer`` and ``extra`` all.
+
+Checkpoint Saving Directory Structure
+-------------------------------------
+
+Commonly, we use the ``default_local_dir`` declared in ``ppo_trainer.yaml`` or ``ppo_megatron_trainer.yml``
+to work as preffix when saving checkpoints, which is ``checkpoints/${trainer.project_name}/${trainer.experiment_name}``.
+
+So the inner checkpoint structure of **FSDP** is like:
+
+.. code::
+
+    checkpoints/${trainer.project_name}/${trainer.experiment_name}
+    ├── global_steps_${i}
+    │   ├── actor
+    │   │   ├── model_world_size_{self.world_size}_rank_{self.rank}.pt
+    │   │   ├── optim_world_size_{self.world_size}_rank_{self.rank}.pt
+    │   │   └── extra_state_world_size_{self.world_size}_rank_{self.rank}.pt
+    │   ├── actor_huggingface
+    │   ├── critic
+    │   │   ├── model_world_size_{self.world_size}_rank_{self.rank}.pt
+    │   │   ├── optim_world_size_{self.world_size}_rank_{self.rank}.pt
+    │   │   └── extra_state_world_size_{self.world_size}_rank_{self.rank}.pt
+    │   └── critic_huggingface
+    └── latest_checkpointed_iteration.txt
+
+All model shards, optimizers and extra states are stored togather, in a sharded and distributed way.
+
+While **Megatron** current checkpoint structure is:
+
+.. code::
+
+    checkpoints/${trainer.project_name}/${trainer.experiment_name}
+    ├── global_steps_${i}
+    │   ├── actor
+    │   │   ├── huggingface     # default save tokenizer, save huggingface model if include ``hf_mode`` in checkpoint.contents
+    │   │   ├── model           # save sharded model, naming the same as Megatron
+    │   │   │   ├── mp_rank_xx_yyy          # xx is tp_rank in 2 digits, yyy is pp_rank in 3 digits
+    │   │   │   │   └── model_states.pt
+    │   │   │   └── mp_rank_xx_xxx
+    │   │   ├── optim
+    │   │   │   ├── distrib_optim_pp{x}_tp{y}.pt
+    │   │   │   └── distrib_optim_pp{x}_tp{y}.pt
+    │   │   └── rng_states
+    │   └── critic
+    │   │   ├── huggingface
+    │   │   ├── model
+    │   │   ├── optim
+    │   │   └── rng_states
+    └── latest_checkpointed_iteration.txt
+
+Convert FSDP and Megatron Checkpoints to HuggingFace Format Model
+-----------------------------------------------------------------
+
+We provide a tool to convert the FSDP and Megatron checkpoints to HuggingFace format model.
+The tool is located in ``scripts/model_merger.py``.
+
+The arguments are as follows:
+
+.. code:: bash
+
+    usage: model_merger.py [-h] [--backend {fsdp,megatron}]
+                           [--tie-word-embedding whether the model share embedding weights]
+                           [--is-value-model whether the model is critic model]
+                           [--hf_model_path $original_model_path, like {Qwen/Qwen2-7B}]
+                           [--local_dir $local_directory saved fsdp or megatron models]
+                           [--target_dir $target_dir to save converted models, default is tmp]
+                           [--hf_upload_path $huggingface_repo to upload]
+
+So example use of Megatron model merger is:
+
+.. code:: bash
+
+    python3 scripts/model_merger.py --backend megatron \
+        --is-value-model \
+        --hf_model_path Qwen/Qwen2-7B \
+        --local_dir checkpoints/verl_megatron_gsm8k_examples/deepseek_megatron_checkpoint_saveload/global_step_1/actor/model
+
+Megatron Merger details
+-----------------------
+
+Current implement of decoder layers uses ``nn.ModuleList`` to store the layers, 
+and thus the model layers on every PP rank and VPP rank starts their index from 0.
+
+There are 3 ways to correct this behavior:
+
+1. Modify the decoder layer's state_dict, add ``offset`` to each layer's index, thus rewrite ``nn.ModuleList`` implementation.
+2. Modify the layer index when saving checkpoint and recover them when loading checkpoint.
+3. The Checkpoint merger do this work, calculate the actual ``offset`` from ``state_dict`` only, a little complex.
+
+Current implementation use solution 2.
+
+Original Checkpoint Utils
+-------------------------
+
+Original Checkpoint Utils refer to original checkpoint implementation in ``verl/models/[model]/megatron/checkpoint_utils``.
+
+We only need ``[model]_loader.py`` in original checkpoint utils now, since we get rid of storing ``hf_model`` every time (which is not recommended for large model training, try only saving sharded models if you can).
+
+.. note:: 
+
+    Note that ``[model]_loader`` only support environments where **storage clusters are able to connect with every calculation nodes**. 
+    Because it utilizes **sharded load way to minimize the loading checkpoint overhead**. 
+    Every rank loads its own data from ``state_dict`` which can be accessed by all of them.
+    While there is also no need to broadcast among DP ranks, since the saved state_dict is only produced by DP rank 0.
+
+    For users who can **only place the huggingface model on one device**, we keep the original costly implementation in ``[model]_loader_deprecated``. In this implementation, rank 0 broadcast all weights to each tp and pp rank, and then dp rank 0 broadcast to all dp ranks. There may be at risks of OOM.
+
+    To use deprecated loader, change the import package of ``load_state_dict_to_megatron_llama``.

docs/advance/dpo_extension.rst

@@ -0,0 +1,271 @@
+Extend to other RL(HF) algorithms
+=================================
+
+We already implemented the complete training pipeline of the PPO
+algorithms. To extend to other algorithms, we analyze the high-level
+principle to use verl and provide a tutorial to implement the DPO
+algorithm. Users can follow the similar paradigm to extend to other RL algorithms.
+
+.. note:: **Key ideas**: Single process drives multi-process computation and data communication.
+
+Overall Approach
+----------------
+
+Step 1: Consider what multi-machine multi-GPU computations are needed
+for each model, such as ``generate_sequence`` , ``compute_log_prob`` and
+``update_policy`` in the actor_rollout model. Implement distributed
+single-process-multiple-data (SPMD) computation and encapsulate them
+into APIs
+
+Step 2: Based on different distributed scenarios, including FSDP and 3D
+parallelism in Megatron-LM, implement single-process control of data
+interaction among multi-process computations.
+
+Step 3: Utilize the encapsulated APIs to implement the control flow
+
+Example: Online DPO
+-------------------
+
+We use verl to implement a simple online DPO algorithm. The algorithm
+flow of Online DPO is as follows:
+
+1. There is a prompt (rollout) generator which has the same weight as
+   the actor model. After a batch of prompts are fed into the generator,
+   it generates N responses for each prompt.
+2. Send all the prompts + responses to a verifier for scoring, which can
+   be reward model or a rule-based function. Then sort them in pairs to
+   form a training batch.
+3. Use this training batch to train the actor model using DPO. During
+   the process, a reference policy is needed.
+
+Step 1: What are the multi-machine multi-GPU computations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**Sample Generator**
+
+Implementation details:
+
+.. code:: python
+
+   from verl.single_controller.base import Worker
+   from verl.single_controller.ray import RayWorkerGroup, RayClassWithInitArgs, RayResourcePool
+   import ray
+
+   @ray.remote
+   class SampleGenerator(Worker):
+       def __init__(self, config):
+           super().__init__()
+           self.config = config
+           
+       def generate_sequences(self, data):
+           pass
+
+Here, ``SampleGenerator`` can be viewed as a multi-process pulled up by
+``torchrun``, with each process running the same code (SPMD).
+``SampleGenerator`` needs to implement a ``generate_sequences`` API for
+the control flow to call. The implementation details inside can use any
+inference engine including vllm, sglang and huggingface. Users can
+largely reuse the code in
+verl/verl/workers/rollout/vllm_rollout/vllm_rollout.py and we won't
+go into details here.
+
+**ReferencePolicy inference**
+
+API: compute reference log probability
+
+.. code:: python
+
+   from verl.single_controller.base import Worker
+   import ray
+
+   @ray.remote
+   class ReferencePolicy(Worker):
+       def __init__(self):
+           super().__init__()
+           self.model = Model()
+           
+       def infer(self, data):
+           return self.model(data)
+
+**Actor update**
+
+API: Update actor model parameters
+
+.. code:: python
+
+   from verl.single_controller.base import Worker
+   import ray
+
+   @ray.remote
+   class DPOActor(Worker):
+       def __init__(self):
+           super().__init__()
+           self.model = Model()
+           self.model = FSDP(self.model)  # or other distributed strategy
+           self.optimizer = optim.Adam(self.model.parameters(), lr=1e-3)
+           self.loss_fn = xxx
+           
+       def update(self, data):
+           self.optimizer.zero_grad()
+           logits = self.model(data)
+           loss = self.loss_fn(logits)
+           loss.backward()
+           self.optimizer.step()
+
+**Notes: How to distinguish between control processes and distributed computation processes**
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Control processes are generally functions directly decorated with
+  ``@ray.remote``
+- Computation processes are all wrapped into a ``RayWorkerGroup``.
+
+Users can reuse most of the distribtued computation logics implemented
+in PPO algorithm, including FSDP and Megatron-LM backend in
+verl/verl/trainer/ppo.
+
+Step 2: Based on different distributed scenarios, implement single-process control of multi-process data interaction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**The core problem to solve here is how a single process sends data to
+multiple processes, drives multi-process computation, and how the
+control process obtains the results of multi-process computation.**
+First, we initialize the multi-process ``WorkerGroup`` in the control
+process.
+
+.. code:: python
+
+   @ray.remote(num_cpus=1)
+   def main_task(config):
+       # construct SampleGenerator
+       resource_pool = RayResourcePool(process_on_nodes=[8] * 2)  # 16 GPUs
+       ray_cls = RayClassWithInitArgs(SampleGenerator, config=config)
+       # put SampleGenerator onto resource pool
+       worker_group = RayWorkerGroup(resource_pool, ray_cls)
+       
+       # construct reference policy
+
+As we can see, in the control process, multiple processes are wrapped
+into a ``RayWorkerGroup``. Inside this ``WorkerGroup``, there is a
+``self._workers`` member, where each worker is a RayActor
+(https://docs.ray.io/en/latest/ray-core/actors.html) of SampleGenerator.
+ray_trainer.md also provide an implementation of
+``MegatronRayWorkerGroup``.
+
+Assuming the model is distributed using FSDP, and there is a batch of
+data on the control process, for data parallelism, the underlying
+calling process is:
+
+.. code:: python
+
+   data = xxx
+   data_list = data.chunk(dp_size)
+
+   output = []
+   for d in data_list:
+       # worker_group._workers[i] is a SampleGenerator
+       output.append(worker_group._workers[i].generate_sequences.remote(d))
+
+   output = ray.get(output)
+   output = torch.cat(output)
+
+Single process calling multiple processes involves the following 3
+steps:
+
+1. Split the data into DP parts on the control process.
+2. Send the data to remote, call the remote computation through RPC, and
+   utilize multi-process computation.
+3. Obtain the computation results of each worker on the control process
+   and merge them.
+
+Frequently calling these 3 steps on the controller process greatly hurts
+code readability. **In verl, we have abstracted and encapsulated these 3
+steps, so that the worker's method + dispatch + collect can be
+registered into the worker_group**
+
+.. code:: python
+
+   from verl.single_controller.base.decorator import register
+
+   def dispatch_data(worker_group, data):
+       return data.chunk(worker_group.world_size)
+       
+   def collect_data(worker_group, data):
+       return torch.cat(data)
+
+   dispatch_mode = {
+       'dispatch_fn': dispatch_data,
+       'collect_fn': collect_data
+   }
+
+   @register(dispatch_mode=dispatch_mode)
+   def generate_sequences(self, data):
+       pass
+
+In this way, we can directly call the method inside the worker through
+the ``worker_group`` on the control (driver) process (which is a single
+process):
+
+.. code:: python
+
+   output = worker_group.generate_sequences(data)
+
+This single line includes data splitting, data distribution and
+computation, and data collection.
+
+Furthermore, the model parallelism size of each model is usually fixed,
+including dp, tp, pp. So for these common distributed scenarios, we have
+pre-implemented specific dispatch and collect methods,in `decorator.py <https://github.com/volcengine/verl/blob/main/verl/single_controller/base/decorator.py>`_, which can be directly used to wrap the computations.
+
+.. code:: python
+
+   from verl.single_controller.base.decorator import register, Dispatch
+
+   @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
+   def generate_sequences(self, data: DataProto) -> DataProto:
+       pass
+
+Here it requires the data interface to be ``DataProto``. Definition of
+``DataProto`` is in `protocol.py <https://github.com/volcengine/verl/blob/main/verl/protocol.py>`_.
+
+Step 3: Main training loop
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+With the above training flows, we can implement the algorithm's control
+flow. It is recommended that ``main_task`` is also a ray remote process.
+
+.. code:: python
+
+   @ray.remote(num_cpus=1)
+   def main_task(config):
+       # construct SampleGenerator
+       resource_pool = RayResourcePool(process_on_nodes=[8] * 2)  # 16 GPUs
+       ray_cls = RayClassWithInitArgs(SampleGenerator, config=config) 
+       # put SampleGenerator onto resource pool
+       sample_gen = RayWorkerGroup(resource_pool, ray_cls)
+       
+       # construct reference policy
+       ray_cls = RayClassWithInitArgs(ReferencePolicy)
+       ref_policy = RayWorkerGroup(resource_pool, ray_cls)
+       
+       # construct actor
+       ray_cls = RayClassWithInitArgs(DPOActor)  
+       dpo_policy = RayWorkerGroup(resource_pool, ray_cls)
+       
+       dataloader = DataLoader()
+       
+       for data in dataloader:
+           # generate data
+           data = sample_gen.generate_sequences(data)
+           # generate scores for each data 
+           data = generate_scores(data)
+           # generate pairwise data using scores
+           data = generate_pairwise_data(data)
+           # generate ref_log_prob
+           data.batch['ref_log_prob'] = ref_policy.infer(data)
+           # update using dpo
+           dpo_policy.update(data)
+           # logging
+
+Here, different ``WorkerGroups`` can be placed in the same resource pool or
+in different resource pools using ``create_colocated_worker_cls``
+similar as in `ray_trainer.py <https://github.com/volcengine/verl/blob/main/verl/trainer/ppo/ray_trainer.py>`_.

docs/advance/fsdp_extension.rst

@@ -0,0 +1,95 @@
+
+Add models with the FSDP backend
+==================================
+
+Model
+--------------------------
+
+In principle, our FSDP backend can support any HF model and we can
+sychronoize the actor model weight with vLLM using `hf_weight_loader.py <https://github.com/volcengine/verl/blob/main/verl/third_party/vllm/vllm_v_0_6_3/hf_weight_loader.py>`_.
+However, ``hf_weight_loader`` is will gather the full state_dict of a
+model during synchronization, which may cause OOM. We suggest using
+``dtensor_weight_loader`` which gather the full model parameter layer by
+layer to reduce the peak memory usage. We already support dtensor weight
+loader for the models below in `dtensor_weight_loader.py <https://github.com/volcengine/verl/blob/main/verl/third_party/vllm/vllm_v_0_5_4/dtensor_weight_loaders.py>`_.:
+
+- ``GPT2LMHeadModel``
+- ``LlamaForCausalLM``
+- ``LLaMAForCausalLM``
+- ``MistralForCausalLM``
+- ``InternLMForCausalLM``
+- ``AquilaModel``
+- ``AquilaForCausalLM``
+- ``Phi3ForCausalLM``
+- ``GemmaForCausalLM``
+- ``Gemma2ForCausalLM``
+- ``GPTBigCodeForCausalLM``
+- ``Starcoder2ForCausalLM``
+- ``Qwen2ForCausalLM``
+- ``DeepseekV2ForCausalLM``
+
+To implement ``dtensor_weight_loader`` of a model that's supported in
+vLLM, follow the guide of gemma model below:
+
+1. Copy the
+   ``load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]])`` from the vllm model class
+   to ``dtensor_weight_loaders.py``
+2. Modify the arguments to
+   ``(actor_weights: Dict, vllm_model: nn.Module)``
+3. Replace the ``self`` to ``vllm_model``
+4. Add the
+   ``local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)``
+   before each ``param = params_dict[name]`` and modify the following
+   weight loading using ``local_loaded_weight``.
+5. Register the implemented dtensor weight loader to ``__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__``.
+
+.. code-block:: diff
+
+    - def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+    + def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+    -   params_dict = dict(self.named_parameters())
+    +   params_dict = dict(vllm_model.named_parameters())
+        loaded_params = set()
+    -   for name, loaded_weight in weights:
+    +   for name, loaded_weight in actor_weights.items():
+            for (param_name, shard_name, shard_id) in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+    +           local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+    -           weight_loader(param, loaded_weight, shard_id)
+    +           weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id)
+                break
+            else:
+                # lm_head is not used in vllm as it is tied with embed_token.
+                # To prevent errors, skip loading lm_head.weight.
+                if "lm_head.weight" in name:
+                    continue
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+    +           local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight)
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+    -           weight_loader(param, loaded_weight)
+    +           weight_loader(param, local_loaded_weight.to(dtype=param.dtype))
+            loaded_params.add(name)
+        unloaded_params = params_dict.keys() - loaded_params
+        if unloaded_params:
+            raise RuntimeError(
+                "Some weights are not initialized from checkpoints: "
+                f"{unloaded_params}")

docs/advance/megatron_extension.rst

@@ -0,0 +1,26 @@
+Add models with the Megatron-LM backend
+=========================================
+
+Model
+-----------
+
+The most challenging aspect to use the Megatron-LM backend is implementing
+the models for training. Currently, we implement Llama model that
+support data parallelism, tensor parallelism, pipeline parallelism (also
+vPP) and sequence parallelism. We also implement remove padding (sequence packing) on Llama
+model, which can be found in `modeling_llama_megatron.py <https://github.com/volcengine/verl/blob/main/verl/models/llama/megatron/modeling_llama_megatron.py>`_.
+
+To support other model, users are required to implement:
+
+1. Implemnt a model similar to ``modeling_llama_megatron.py`` that satisfy the
+   parallelism requirements of Megatron-LM. Then register your model in
+   the `registry.py <https://github.com/volcengine/verl/blob/main/verl/models/registry.py>`_.
+2. Checkpoint utils that can load full checkpoint (e.g. huggingface
+   checkpoint) to partitioned models during the runtime. Then register
+   your loader to ``weight_loader_registry`` in `weight_loader_registry.py <https://github.com/volcengine/verl/blob/main/verl/models/weight_loader_registry.py>`_.
+3. Weight loader that synchronize the weight from Megatron to rollout
+   (vLLM) model. Note that both the actor model and rollout model are
+   partitioned during runtime. So, it's advisable to map the model name
+   in actor model implementation. Otherwise, you may need an additional
+   name mapping and even weight transformation. The weight loader implementation
+   is in `megatron_weight_loaders.py <https://github.com/volcengine/verl/blob/main/verl/third_party/vllm/vllm_v_0_6_3/megatron_weight_loaders.py>`_.

docs/advance/placement.rst

@@ -0,0 +1,11 @@
+Ray API Design Tutorial
+=======================================
+
+We provide a tutorial for our Ray API design, including:
+
+- Ray basic concepts
+- Resource Pool and RayWorkerGroup
+- Data Dispatch, Execution and Collection
+- Initialize the RayWorkerGroup and execute the distributed computation in the given Resource Pool
+
+See details in `tutorial.ipynb <https://github.com/volcengine/verl/blob/main/examples/ray/tutorial.ipynb>`_.

docs/amd_tutorial/amd_build_dockerfile_page.rst

@@ -0,0 +1,512 @@
+Getting started with AMD (ROCM Kernel)
+=====================================================
+
+Author: `Yusheng Su <https://yushengsu-thu.github.io/>`_
+
+Setup
+-----
+
+If you run on AMD GPUs (MI300) with ROCM platform, you cannot use the previous quickstart to run VeRL. You should follow the following steps to build a docker and assign ``HIP_VISIBLE_DEVICES`` and ``ROCR_VISIBLE_DEVICES`` when starting RLHF training.
+
+
+
+docker/Dockerfile.rocm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    #  Build the docker in the repo dir:
+    # docker build -f docker/Dockerfile.rocm -t verl-rocm:03.04.2015 .
+    # docker images # you can find your built docker
+    FROM rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
+
+    # Set working directory
+    # WORKDIR $PWD/app
+
+    # Set environment variables
+    ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942"
+
+    # Install vllm
+    RUN pip uninstall -y vllm && \
+        rm -rf vllm && \
+        git clone -b v0.6.3 https://github.com/vllm-project/vllm.git && \
+        cd vllm && \
+        MAX_JOBS=$(nproc) python3 setup.py install && \
+        cd .. && \
+        rm -rf vllm
+
+    # Copy the entire project directory
+    COPY . .
+
+    # Install dependencies
+    RUN pip install "tensordict<0.6" --no-deps && \
+        pip install accelerate \
+        codetiming \
+        datasets \
+        dill \
+        hydra-core \
+        liger-kernel \
+        numpy \
+        pandas \
+        datasets \
+        peft \
+        "pyarrow>=15.0.0" \
+        pylatexenc \
+        "ray[data,train,tune,serve]" \
+        torchdata \
+        transformers \
+        wandb \
+        orjson \
+        pybind11 && \
+        pip install -e . --no-deps
+
+Build the image:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    docker build -t verl-rocm .
+
+Run the container
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+Optional: Running without root and with user permissions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: bash
+
+    docker run --rm -it \
+      --device /dev/dri \
+      --device /dev/kfd \
+      -p 8265:8265 \
+      --group-add video \
+      --cap-add SYS_PTRACE \
+      --security-opt seccomp=unconfined \
+      --privileged \
+      -v $HOME/.ssh:/root/.ssh \
+      -v $HOME:$HOME \
+      --shm-size 128G \
+      -w $PWD \
+      verl-rocm \
+      /bin/bash
+
+(Optional): If you do not want to root mode and require assign yuorself as the user
+Please add ``-e HOST_UID=$(id -u)`` and ``-e HOST_GID=$(id -g)`` into the above docker launch script. 
+
+Example
+-------
+
+Due to to special setting in AMD (ROCM) torch, you need to assign ``HIP_VISIBLE_DEVICES`` and ``ROCR_VISIBLE_DEVICES`` when starting Ray in VeRL's RLHF training.
+
+PPO
+~~~
+
+.. code-block:: bash
+
+    YOUR_PROJECT_NAME=r1-verl-ppo-upstream
+    YOUR_RUN_NAME=r1-training_ppo-upstream 
+    # export HYDRA_FULL_ERROR=1
+    export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+    export ROCR_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES
+    GPUS_PER_NODE=8
+    MODEL_PATH=Qwen/Qwen2.5-0.5B-Instruct
+    python3 examples/data_preprocess/gsm8k.py --local_dir data/gsm8k
+    python3 -c "import transformers; transformers.pipeline('text-generation', model='$MODEL_PATH')"
+    PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \
+     data.train_files=data/gsm8k/train.parquet \
+     data.val_files=data/gsm8k/test.parquet \
+     data.train_batch_size=256 \
+     data.val_batch_size=1312 \
+     data.max_prompt_length=512 \
+     data.max_response_length=256 \
+     actor_rollout_ref.model.path=$MODEL_PATH \
+     actor_rollout_ref.actor.optim.lr=1e-6 \
+     actor_rollout_ref.actor.ppo_mini_batch_size=64 \
+     actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
+     actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
+     actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+     actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
+     actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
+     critic.optim.lr=1e-5 \
+     critic.model.path=$MODEL_PATH \
+     critic.ppo_micro_batch_size_per_gpu=4 \
+     algorithm.kl_ctrl.kl_coef=0.001 \
+     trainer.logger=['console'] \
+     trainer.project_name=$YOUR_PROJECT_NAME \
+     trainer.experiment_name=$YOUR_RUN_NAME \
+     trainer.val_before_train=False \
+     trainer.default_hdfs_dir=null \
+     trainer.n_gpus_per_node=$GPUS_PER_NODE \
+     trainer.nnodes=1 \
+     trainer.save_freq=10 \
+     trainer.test_freq=10 \
+     trainer.total_epochs=15 #2>&1 | tee verl_demo.log
+
+GRPO
+~~~~
+
+.. code-block:: bash
+
+    YOUR_PROJECT_NAME=r1-verl-grpo-upstream
+    YOUR_RUN_NAME=r1-training_grpo-upstream
+    # export HYDRA_FULL_ERROR=1
+    # export FSDP_VERBOSE=1 
+    export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+    export ROCR_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES
+    GPUS_PER_NODE=8
+    MODEL_PATH=Qwen/Qwen2.5-0.5B-Instruct
+    # MODEL_PATH=Qwen/Qwen2-7B-Instruct
+    python3 examples/data_preprocess/gsm8k.py --local_dir data/gsm8k
+    python3 -c "import transformers; transformers.pipeline('text-generation', model='$MODEL_PATH')"
+    python3 -m verl.trainer.main_ppo \
+        algorithm.adv_estimator=grpo \
+        data.train_files=data/gsm8k/train.parquet \
+        data.val_files=data/gsm8k/test.parquet \
+        data.train_batch_size=1024 \
+        data.val_batch_size=1312 \
+        data.max_prompt_length=512 \
+        data.max_response_length=1024 \
+        actor_rollout_ref.model.path=$MODEL_PATH \
+        actor_rollout_ref.actor.optim.lr=1e-6 \
+        actor_rollout_ref.model.use_remove_padding=True \
+        actor_rollout_ref.actor.ppo_mini_batch_size=256 \
+        actor_rollout_ref.actor.use_dynamic_bsz=True \
+        actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \
+        actor_rollout_ref.actor.use_kl_loss=True \
+        actor_rollout_ref.actor.kl_loss_coef=0.001 \
+        actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+        actor_rollout_ref.model.enable_gradient_checkpointing=Flase \
+        actor_rollout_ref.actor.fsdp_config.param_offload=False \
+        actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+        actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
+        actor_rollout_ref.rollout.name=vllm \
+        actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
+        actor_rollout_ref.rollout.n=5 \
+        actor_rollout_ref.ref.fsdp_config.param_offload=False \
+        algorithm.kl_ctrl.kl_coef=0.001 \
+        trainer.critic_warmup=0 \
+        trainer.logger=['console'] \
+        trainer.project_name=$YOUR_PROJECT_NAME \
+        trainer.experiment_name=$YOUR_RUN_NAME \
+        trainer.n_gpus_per_node=$GPUS_PER_NODE \
+        trainer.val_before_train=False \
+        trainer.nnodes=1 \
+        trainer.save_freq=-1 \
+        trainer.test_freq=10 \
+        trainer.total_epochs=15
+
+
+
+Multi-node training: slurm with Docker/Podman container
+---------------------------------------------------------------------------------------
+
+If you want to run multi-node training with slurm, you can use the following script. 
+
+.. note::
+    1. You need to use ``podman`` or ``docker`` in the following script. We will release the apptainer script later.
+    2. If you want to use ``podman``, you just replace ``docker`` with ``podman`` in the following script.
+
+The script includes the following steps:
+
+1. SLURM Configuration
+2. Environment Setup
+3. Docker/Podman Container Setup
+4. Ray Cluster Initialization
+5. Data Preprocessing
+6. Model Setup
+7. Training Launch
+
+
+slurm_script.sh
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    #!/bin/bash
+
+    #SBATCH --job-name=verl-ray-on-slurm
+    #SBATCH --nodes=2
+    #SBATCH --ntasks-per-node=2
+    #SBATCH --mem=200G
+    #SBATCH --time=30-00:00:00
+    #SBATCH --gpus-per-node=8
+    #SBATCH --cpus-per-task=28
+    #SBATCH --output=../verl_log/slurm-%j.out
+    #SBATCH --error=../verl_log/slurm-%j.err
+    #SBATCH --nodelist=gpu-[0,1]
+
+
+    # load necessary modules
+    ### Run this setup
+    # [Cluster]: Use docker
+    # docker pull docker.io/rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
+
+
+    ##########################################################################
+    ###The following setting should be set in different project and cluster###
+    ##########################################################################
+
+    ### Project
+    CONTAINER_NAME="multinode_verl_training"
+    IMG="verl.rocm"
+    DOCKERFILE="docker/Dockerfile.rocm"
+    # echo $PWD
+    verl_workdir="${HOME}/projects/verl_upstream"
+    export TRANSFORMERS_CACHE="${HOME}/.cache/huggingface"
+    export HF_HOME=$TRANSFORMERS_CACHE
+
+    ### Cluster Network Setting
+    export NCCL_DEBUG=TRACE
+    export GPU_MAX_HW_QUEUES=2
+    export TORCH_NCCL_HIGH_PRIORITY=1
+    export NCCL_CHECKS_DISABLE=1
+    # export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7 
+    export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
+    export NCCL_IB_GID_INDEX=3
+    export NCCL_CROSS_NIC=0
+    export CUDA_DEVICE_MAX_CONNECTIONS=1
+    export NCCL_PROTO=Simple
+    export RCCL_MSCCL_ENABLE=0
+    export TOKENIZERS_PARALLELISM=false
+    export HSA_NO_SCRATCH_RECLAIM=1
+    ##########################################################################
+
+    ### For rocm and training script
+    export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+    export ROCR_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES
+    export CUDA_VISIBLE_DEVICES=$HIP_VISIBLE_DEVICES
+
+
+    # Build and launch the Docker container
+    srun bash -c "
+        # Exit on any error
+        set -e 
+
+        # Clean up dangling images (images with <none> tag)
+        docker image prune -f
+
+        # Need to pull the docker first
+        docker pull docker.io/rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4
+        
+        if ! docker images --format "{{.Repository}}:{{.Tag}}" | grep -q "${IMG}"; then
+            echo \"Building ${IMG} image...\"
+            docker build -f \"${DOCKERFILE}\" -t \"${IMG}\" .
+        else
+            echo \"${IMG} image already exists, skipping build\"
+        fi
+
+        # Removing old container if exists
+        docker rm \"${CONTAINER_NAME}\" 2>/dev/null || true
+
+        # Checking network devices
+        ibdev2netdev
+
+        # Launch the docker
+        docker run --rm -d \
+        -e HYDRA_FULL_ERROR=1 \
+        -e HIP_VISIBLE_DEVICES=${HIP_VISIBLE_DEVICES} \
+        -e ROCR_VISIBLE_DEVICES=${ROCR_VISIBLE_DEVICES} \
+        -e CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES} \
+        -e NCCL_DEBUG=${NCCL_DEBUG} \
+        -e GPU_MAX_HW_QUEUES=${GPU_MAX_HW_QUEUES} \
+        -e TORCH_NCCL_HIGH_PRIORITY=${TORCH_NCCL_HIGH_PRIORITY} \
+        -e NCCL_CHECKS_DISABLE=${NCCL_CHECKS_DISABLE} \
+        -e NCCL_IB_HCA=${NCCL_IB_HCA} \
+        -e NCCL_IB_GID_INDEX=${NCCL_IB_GID_INDEX} \
+        -e NCCL_CROSS_NIC=${NCCL_CROSS_NIC} \
+        -e CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS} \
+        -e NCCL_PROTO=${NCCL_PROTO} \
+        -e RCCL_MSCCL_ENABLE=${RCCL_MSCCL_ENABLE} \
+        -e TOKENIZERS_PARALLELISM=${TOKENIZERS_PARALLELISM} \
+        -e HSA_NO_SCRATCH_RECLAIM=${HSA_NO_SCRATCH_RECLAIM} \
+        -e TRANSFORMERS_CACHE=${TRANSFORMERS_CACHE} \
+        -e HF_HOME=${HF_HOME} \
+        --network host \
+        --device /dev/dri \
+        --device /dev/kfd \
+        --device /dev/infiniband \
+        --group-add video \
+        --cap-add SYS_PTRACE \
+        --security-opt seccomp=unconfined \
+        --privileged \
+        -v \${HOME}:\${HOME} \
+        -v \${HOME}/.ssh:/root/.ssh \
+        -w "${verl_workdir}" \
+        --shm-size 128G \
+        --name \"${CONTAINER_NAME}\" \
+        \"${IMG}\" \
+        tail -f /dev/null
+
+        echo \"Container setup completed\"
+    "
+        # (Optional): If you do not want to root mode and require assign yuorself as the user
+        # Please add `-e HOST_UID=$(id -u)` and `-e HOST_GID=$(id -g)` into the above docker launch script. 
+
+
+
+
+
+    ### Ray launch the nodes before training
+
+    # Getting the node names
+    nodes_array=($(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' '))
+
+    head_node=${nodes_array[0]}
+    head_node_ip=$(srun --nodes=1 --ntasks=1 -w "$head_node" hostname --ip-address)
+
+    # if we detect a space character in the head node IP, we'll
+    # convert it to an ipv4 address. This step is optional.
+    if [[ "$head_node_ip" == *" "* ]]; then
+        IFS=' ' read -ra ADDR <<<"$head_node_ip"
+    if [[ ${#ADDR[0]} -gt 16 ]]; then
+        head_node_ip=${ADDR[1]}
+    else
+        head_node_ip=${ADDR[0]}
+    fi
+        echo "IPV6 address detected. We split the IPV4 address as $head_node_ip"
+    fi
+
+    port=6379
+    ip_head=$head_node_ip:$port
+    export ip_head
+    echo "IP Head: $ip_head"
+
+    # make sure we set environment variables before Ray initialization
+    export VLLM_ATTENTION_BACKEND=XFORMERS
+
+    # Print out all env variables
+    printenv
+
+    echo "Starting HEAD at $head_node"
+    srun --nodes=1 --ntasks=1 -w "$head_node" \
+        docker exec "${CONTAINER_NAME}" \
+            ray start --head --node-ip-address="$head_node_ip" --port=$port \
+            --dashboard-port=8266 \
+            --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block &
+    # optional, though may be useful in certain versions of Ray < 1.0.
+    sleep 10
+
+    # number of nodes other than the head node
+    worker_num=$((SLURM_JOB_NUM_NODES - 1))
+
+    for ((i = 1; i <= worker_num; i++)); do
+        node_i=${nodes_array[$i]}
+        echo "Debug: Starting worker on node_i = ${node_i}"
+        if [ -z "$node_i" ]; then
+            echo "Error: Empty node name for worker $i"
+            continue
+        fi
+        echo "Starting WORKER $i at $node_i"
+        srun --nodes=1 --ntasks=1 -w "$node_i" \
+            docker exec "${CONTAINER_NAME}" \
+                ray start --address "$ip_head" --num-cpus "${SLURM_CPUS_PER_TASK}" --num-gpus "${SLURM_GPUS_PER_NODE}" --block &
+        sleep 5
+    done
+
+
+
+
+    # Ray initlization test (See whether any error in the above excution)
+    echo "Testing Ray initialization in the slurm nodes..."
+    docker exec "${CONTAINER_NAME}" python3 -c '
+    import ray
+    try:
+        ray.init(address="auto")
+        print("\n=== Ray Cluster Status ===")
+        print(f"Number of nodes: {len(ray.nodes())}")
+        for node in ray.nodes():
+            print("Node: {}, Status: {}".format(node["NodeManagerHostname"], node["Alive"]))
+            # print(f"Node: {node}")
+        ray.shutdown()
+        print("Ray initialization successful!")
+    except Exception as e:
+        print(f"Ray initialization failed: {str(e)}")
+    '
+    echo "=== Ray test completed ==="
+    ######
+
+
+
+    # Run data preprocessing
+
+    echo "Starting data preprocessing..."
+    docker exec "${CONTAINER_NAME}" \
+        python3 "examples/data_preprocess/gsm8k.py" "--local_dir" "../data/gsm8k"
+
+    echo "Starting data preprocessing..."
+    docker exec "${CONTAINER_NAME}" \
+        python3 "examples/data_preprocess/math_dataset.py" "--local_dir" "../data/math"
+
+    train_files="../data/gsm8k/train.parquet"
+    val_files="../data/gsm8k/test.parquet"
+
+    # Download and test model
+    echo "Loading model..."
+    docker exec "${CONTAINER_NAME}" \
+        python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2-7B-Instruct')"
+    MODEL_PATH="Qwen/Qwen2-7B-Instruct"
+
+    # Set model path after pipeline test
+    MODEL_PATH="Qwen/Qwen2.5-0.5B-Instruct"
+
+    echo "== Data and model loading Done =="
+
+    echo "Start to train..."
+
+    docker exec "${CONTAINER_NAME}" \
+        python3 -c "import transformers; transformers.pipeline('text-generation', model='Qwen/Qwen2-7B-Instruct')"
+    MODEL_PATH="Qwen/Qwen2-7B-Instruct"
+
+
+    PYTHONUNBUFFERED=1 srun --overlap --nodes=${SLURM_NNODES} --ntasks=1 -w "$head_node" \
+        docker exec "${CONTAINER_NAME}" \
+        python3 -m verl.trainer.main_ppo \
+        data.train_files=$train_files \
+        data.val_files=$val_files \
+        data.train_batch_size=1024 \
+        data.max_prompt_length=1024 \
+        data.max_response_length=1024 \
+        actor_rollout_ref.model.path=$MODEL_PATH \
+        actor_rollout_ref.model.enable_gradient_checkpointing=False \
+        actor_rollout_ref.actor.optim.lr=1e-6 \
+        actor_rollout_ref.model.use_remove_padding=True \
+        actor_rollout_ref.actor.ppo_mini_batch_size=256 \
+        actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \
+        actor_rollout_ref.model.enable_gradient_checkpointing=True \
+        actor_rollout_ref.actor.fsdp_config.param_offload=False \
+        actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+        actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \
+        actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
+        actor_rollout_ref.rollout.name=vllm \
+        actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \
+        actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=16 \
+        actor_rollout_ref.ref.fsdp_config.param_offload=True \
+        critic.optim.lr=1e-5 \
+        critic.model.use_remove_padding=True \
+        critic.model.path=$MODEL_PATH \
+        critic.model.enable_gradient_checkpointing=False \
+        critic.ppo_micro_batch_size_per_gpu=8 \
+        critic.model.fsdp_config.param_offload=False \
+        critic.model.fsdp_config.optimizer_offload=False \
+        algorithm.kl_ctrl.kl_coef=0.0001 \
+        trainer.critic_warmup=0 \
+        trainer.logger=['console','wandb'] \
+        trainer.project_name='verl_example' \
+        trainer.experiment_name='Qwen2.5-32B-Instruct_function_rm' \
+        trainer.n_gpus_per_node=${SLURM_GPUS_PER_NODE} \
+        trainer.val_before_train=False \
+        trainer.nnodes=${SLURM_NNODES} \
+        trainer.save_freq=-1 \
+        trainer.test_freq=10 \
+        trainer.total_epochs=15
+
+
+Run slurm_script.sh
+~~~~~~~~~~~~~~~~~~~~
+Just sbatch your slurm_script.sh
+
+.. code-block:: bash
+
+    sbatch slurm_script.sh
+

docs/conf.py

@@ -0,0 +1,83 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = u'verl'
+# pylint: disable=W0622
+copyright = u'2024 ByteDance Seed Foundation MLSys Team'
+author = u'Guangming Sheng, Chi Zhang, Yanghua Peng, Haibin Lin'
+
+
+# -- General configuration ---------------------------------------------------
+# The master toctree document.
+master_doc = 'index'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ['recommonmark',
+  'sphinx.ext.autodoc',
+  'sphinx.ext.autosummary',
+  'sphinx.ext.autosectionlabel',
+]
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+source_suffix = ['.rst', 'rest', '.md']
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = u'en'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']

docs/data.rst

@@ -0,0 +1,59 @@
+Data interface
+=========================
+
+DataProto is the interface for data exchange.
+
+The :class:`verl.DataProto` class contains two key members:
+
+- batch: a :class:`tensordict.TensorDict` object for the actual data
+- meta_info: a :class:`Dict` with additional meta information
+
+TensorDict
+~~~~~~~~~~~~
+
+:attr:`DataProto.batch` is built on top of :class:`tensordict`, a project in the PyTorch ecosystem.
+A TensorDict is a dict-like container for tensors. To instantiate a TensorDict, you must specify key-value pairs as well as the batch size.
+
+.. code-block:: python
+
+    >>> import torch
+    >>> from tensordict import TensorDict
+    >>> tensordict = TensorDict({"zeros": torch.zeros(2, 3, 4), "ones": torch.ones(2, 3, 5)}, batch_size=[2,])
+    >>> tensordict["twos"] = 2 * torch.ones(2, 5, 6)
+    >>> zeros = tensordict["zeros"]
+    >>> tensordict
+    TensorDict(
+    fields={
+        ones: Tensor(shape=torch.Size([2, 3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
+        twos: Tensor(shape=torch.Size([2, 5, 6]), device=cpu, dtype=torch.float32, is_shared=False),
+        zeros: Tensor(shape=torch.Size([2, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False)},
+    batch_size=torch.Size([2]),
+    device=None,
+    is_shared=False)
+
+One can also index a tensordict along its batch_size. The contents of the TensorDict can be manipulated collectively as well.
+
+.. code-block:: python
+
+    >>> tensordict[..., :1]
+    TensorDict(
+    fields={
+        ones: Tensor(shape=torch.Size([1, 3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
+        twos: Tensor(shape=torch.Size([1, 5, 6]), device=cpu, dtype=torch.float32, is_shared=False),
+        zeros: Tensor(shape=torch.Size([1, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False)},
+    batch_size=torch.Size([1]),
+    device=None,
+    is_shared=False)
+    >>> tensordict = tensordict.to("cuda:0")
+    >>> tensordict = tensordict.reshape(6)
+
+For more about :class:`tensordict.TensorDict` usage, see the official tensordict_ documentation.
+
+.. _tensordict: https://pytorch.org/tensordict/overview.html
+
+
+Core APIs
+~~~~~~~~~~~~~~~~~
+
+.. autoclass::  verl.DataProto
+   :members: to, select, union, make_iterator, concat